Development of a predictive machine learning model for pathogen profiles in patients with secondary immunodeficiency.

BACKGROUND: Secondary immunodeficiency can arise from various clinical conditions that include HIV infection, chronic diseases, malignancy and long-term use of immunosuppressives, which makes the suffering patients susceptible to all types of pathogenic infections. Other than HIV infection, the possible pathogen profiles in other aetiology-induced secondary immunodeficiency are largely unknown. METHODS: Medical records of the patients with secondary immunodeficiency caused by various aetiologies were collected from the First Affiliated Hospital of Nanchang University, China. Based on these records, models were developed with the machine learning method to predict the potential infectious pathogens that may inflict the patients with secondary immunodeficiency caused by various disease conditions other than HIV infection. RESULTS: Several metrics were used to evaluate the models' performance. A consistent conclusion can be drawn from all the metrics that Gradient Boosting Machine had the best performance with the highest accuracy at 91.01%, exceeding other models by 13.48, 7.14, and 4.49% respectively. CONCLUSIONS: The models developed in our study enable the prediction of potential infectious pathogens that may affect the patients with secondary immunodeficiency caused by various aetiologies except for HIV infection, which will help clinicians make a timely decision on antibiotic use before microorganism culture results return.

KPCA-WRF-prediction of heart rate using deep feature fusion and machine learning classification with tuned weighted hyper-parameter.

The rapid advancement of technology such as stream processing technologies, deep-learning approaches, and artificial intelligence plays a prominent and vital role, to detect heart rate using a prediction model. However, the existing methods could not handle high -dimensional datasets, and deep feature learning to improvise the performance. Therefore, this work proposed a real-time heart rate prediction model, using K-nearest neighbour (KNN) adhered to the principle component analysis algorithm (PCA) and weighted random forest algorithm for feature fusion (KPCA-WRF) approach and deep CNN feature learning framework. The feature selection, from the fused features, was optimized by ant colony optimization (ACO) and particle swarm optimization (PSO) algorithm to enhance the selected fused features from deep CNN. The optimized features were reduced to low dimensions using the PCA algorithm. The significant straight heart rate features are plotted by capturing out nearest similar data point values using the algorithm. The fused features were then classified for aiding the training process. The weighted values are assigned to those tuned hyper parameters (feature matrix forms). The optimal path and continuity of the weighted feature representations are moved using the random forest algorithm, in K-fold validation iterations.

Quantum and Quantum-Inspired Stereographic K Nearest-Neighbour Clustering.

Nearest-neighbour clustering is a simple yet powerful machine learning algorithm that finds natural application in the decoding of signals in classical optical-fibre communication systems. Quantum k-means clustering promises a speed-up over the classical k-means algorithm; however, it has been shown to not currently provide this speed-up for decoding optical-fibre signals due to the embedding of classical data, which introduces inaccuracies and slowdowns. Although still not achieving an exponential speed-up for NISQ implementations, this work proposes the generalised inverse stereographic projection as an improved embedding into the Bloch sphere for quantum distance estimation in k-nearest-neighbour clustering, which allows us to get closer to the classical performance. We also use the generalised inverse stereographic projection to develop an analogous classical clustering algorithm and benchmark its accuracy, runtime and convergence for decoding real-world experimental optical-fibre communication data. This proposed 'quantum-inspired' algorithm provides an improvement in both the accuracy and convergence rate with respect to the k-means algorithm. Hence, this work presents two main contributions. Firstly, we propose the general inverse stereographic projection into the Bloch sphere as a better embedding for quantum machine learning algorithms; here, we use the problem of clustering quadrature amplitude modulated optical-fibre signals as an example. Secondly, as a purely classical contribution inspired by the first contribution, we propose and benchmark the use of the general inverse stereographic projection and spherical centroid for clustering optical-fibre signals, showing that optimizing the radius yields a consistent improvement in accuracy and convergence rate.

Development of an image classification pipeline for atherosclerotic plaques assessment using supervised machine learning.

BACKGROUND: During atherosclerosis, the narrowing of the arterial lumen is observed through the accumulation of bio compounds and the formation of plaque within artery walls. A non-linear optical imaging modality (NLOM), coherent anti-stokes Raman scattering (CARS) microscopy, can be used to image lipid-rich structures commonly found in atherosclerotic plaques. By matching the lipid's molecular vibrational frequencies (CH bonds), it is possible to map the accumulation of lipid-rich structures without the need for exogenous labelling and/or processing of the samples. CARS allows for the visualization of the morphological features of plaque. In combination with supervised machine learning, CARS imaged morphological features can be used to characterize the progression of atherosclerotic plaques.  RESULTS: Based on a set of label-free CARS images of atherosclerotic plaques (i.e. foam cell clusters) from a Watanabe heritable hyperlipidemic rabbit model, we developed an automated pipeline to classify atherosclerotic lesions based on their major morphological features. Our method uses image preprocessing to first improve the quality of the CARS-imaged plaque, followed by the segmentation of the plaque using Otsu thresholding, marker-controlled watershed, K-means segmentation and a novel independent foam cell thresholding segmentation. To define relevant morphological features, 27 quantitative features were extracted and further refined by a novel coefficient of variation feature refinement method in accordance with filter-type feature selection. Refined morphological features were supplied into three supervised machine learning algorithms; K-nearest neighbour, support vector machine and decision tree classifier. The classification pipeline showcased the ability to exploit relevant plaque morphological features to accurately classify 3 pre-defined stages of atherosclerosis: early fatty streak development (EFS) and advancing atheroma (AA) with a greater than 85% class accuracy CONCLUSIONS: Through the combination of CARS microscopy and computational methods, a powerful classification tool was developed to identify the progression of atherosclerotic plaque in an automated manner. Using a curated dataset, the classification pipeline demonstrated the ability to differentiate between EFS, EF and AA. Thus, presenting the opportunity to classify the onset of atherosclerosis at an earlier stage of development.

Application of kNN and SVM to predict the prognosis of advanced schistosomiasis.

Predictive models for prognosis of small sample advanced schistosomiasis patients have not been well studied. We aimed to construct prognostic predictive models of small sample advanced schistosomiasis patients using two machine learning algorithms, k nearest neighbour (kNN) and support vector machine (SVM) utilising routinely available data under the government medical assistance programme. The predictive models were derived from 229 patients from Xiantao and externally validated by 77 patients of Jiayu, two county-level cities in Hubei province, China. Candidate predictors were selected according to expert opinions and literature reports, including clinical features, sociodemographic characteristics, and medical examinations results. An area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the models' predictive performances. The AUC values were 0.879 for the kNN model and 0.890 for the SVM model in the training set, 0.852 for the kNN model, and 0.785 for the SVM model in the external validation set. The kNN and SVM models can be used to improve the health services provided by healthcare planners, clinicians, and policymakers.

In-Field Detection of American Foulbrood (AFB) by Electric Nose Using Classical Classification Techniques and Sequential Neural Networks.

American foulbrood is a dangerous bee disease that attacks the sealed brood. It quickly leads to the death of bee colonies. Efficient diagnosis of this disease is essential. As specific odours are produced when larvae rot, it was investigated whether an electronic nose can distinguish between colonies affected by American foulbrood and healthy ones. The experiment was conducted in an apiary with 18 bee families, 9 of which showed symptoms of the disease confirmed by laboratory diagnostics. Three units of the Beesensor V.2 device based on an array of six semiconductor TGS gas sensors, manufactured by Figaro, were tested. Each copy of the device was tested in all bee colonies: sick and healthy. The measurement session per bee colony lasted 40 min and yielded results from four 10 min measurements. One 10-min measurement consisted of a 5 min regeneration phase and a 5 min object-measurement phase. For the experiments, we used both classical classification methods such as k-nearest neighbour, Naive Bayes, Support Vector Machine, discretized logistic regression, random forests, and committee of classifiers, that is, methods based on extracted representative data fragments. We also used methods based on the entire 600 s series, in this study of sequential neural networks. We considered, in this study, six options for data preparation as part of the transformation of data series into representative results. Among others, we used single stabilised sensor readings as well as average values from stable areas. For verifying the quality of the classical classifiers, we used the 25-fold train-and-test method. The effectiveness of the tested methods reached a threshold of 75 per cent, with results stable between 65 and 70 per cent. As an element to confirm the possibility of class separation using an artificial nose, we used applied visualisations of classes. It is clear from the experiments conducted that the artificial nose tested has practical potential. Our experiments show that the approach to the problem under study by sequential network learning on a sequence of data is comparable to the best classical methods based on discrete data samples. The results of the experiment showed that the Beesensor V.2 along with properly selected classification techniques can become a tool to facilitate rapid diagnosis of American foulbrood under field conditions.

Ultra-Wideband Radar-Based Indoor Activity Monitoring for Elderly Care.

In this paper, we propose an unobtrusive method and architecture for monitoring a person's presence and collecting his/her health-related parameters simultaneously in a home environment. The system is based on using a single ultra-wideband (UWB) impulse-radar as a sensing device. Using UWB radars, we aim to recognize a person and some preselected movements without camera-type monitoring. Via the experimental work, we have also demonstrated that, by using a UWB signal, it is possible to detect small chest movements remotely to recognize coughing, for example. In addition, based on statistical data analysis, a person's posture in a room can be recognized in a steady situation. In addition, we implemented a machine learning technique (k-nearest neighbour) to automatically classify a static posture using UWB radar data. Skewness, kurtosis and received power are used in posture classification during the postprocessing. The classification accuracy achieved is more than 99%. In this paper, we also present reliability and fault tolerance analyses for three kinds of UWB radar network architectures to point out the weakest item in the installation. This information is highly important in the system's implementation.

Recognition of Pharmacological Bi-Heterocyclic Compounds by Using Terahertz Time Domain Spectroscopy and Chemometrics.

In this study, we presented the concept and implementation of a fully functional system for the recognition of bi-heterocyclic compounds. We have conducted research into the application of machine learning methods to correctly recognize compounds based on THz spectra, and we have described the process of selecting optimal parameters for the kernel support vector machine (KSVM) with an additional `unknown' class. The chemical compounds used in the study contain a target molecule, used in pharmacy to combat inflammatory states formed in living organisms. Ready-made medical products with similar properties are commonly referred to as non-steroidal anti-inflammatory drugs (NSAIDs) once authorised on the pharmaceutical market. It was crucial to clearly determine whether the tested sample is a chemical compound known to researchers or is a completely new structure which should be additionally tested using other spectrometric methods. Our approach allows us to achieve 100% accuracy of the classification of the tested chemical compounds in the time of several milliseconds counted for 30 samples of the test set. It fits perfectly into the concept of rapid recognition of bi-heterocyclic compounds without the need to analyse the percentage composition of compound components, assuming that the sample is classified in a known group. The method allows us to minimize testing costs and significant reduction of the time of analysis.

Covariate shift estimation based adaptive ensemble learning for handling non-stationarity in motor imagery related EEG-based brain-computer interface.

The non-stationary nature of electroencephalography (EEG) signals makes an EEG-based brain-computer interface (BCI) a dynamic system, thus improving its performance is a challenging task. In addition, it is well-known that due to non-stationarity based covariate shifts, the input data distributions of EEG-based BCI systems change during inter- and intra-session transitions, which poses great difficulty for developments of online adaptive data-driven systems. Ensemble learning approaches have been used previously to tackle this challenge. However, passive scheme based implementation leads to poor efficiency while increasing high computational cost. This paper presents a novel integration of covariate shift estimation and unsupervised adaptive ensemble learning (CSE-UAEL) to tackle non-stationarity in motor-imagery (MI) related EEG classification. The proposed method first employs an exponentially weighted moving average model to detect the covariate shifts in the common spatial pattern features extracted from MI related brain responses. Then, a classifier ensemble was created and updated over time to account for changes in streaming input data distribution wherein new classifiers are added to the ensemble in accordance with estimated shifts. Furthermore, using two publicly available BCI-related EEG datasets, the proposed method was extensively compared with the state-of-the-art single-classifier based passive scheme, single-classifier based active scheme and ensemble based passive schemes. The experimental results show that the proposed active scheme based ensemble learning algorithm significantly enhances the BCI performance in MI classifications.

Optimization of the KNN Supervised Classification Algorithm as a Support Tool for the Implantation of Deep Brain Stimulators in Patients with Parkinson's Disease.

Deep Brain Stimulation (DBS) of the Subthalamic Nuclei (STN) is the most used surgical treatment to improve motor skills in patients with Parkinson's Disease (PD) who do not adequately respond to pharmacological treatment, or have related side effects. During surgery for the implantation of a DBS system, signals are obtained through microelectrodes recordings (MER) at different depths of the brain. These signals are analyzed by neurophysiologists to detect the entry and exit of the STN region, as well as the optimal depth for electrode implantation. In the present work, a classification model is developed and supervised by the K-nearest neighbour algorithm (KNN), which is automatically trained from the 18 temporal features of MER registers of 14 patients with PD in order to provide a clinical support tool during DBS surgery. We investigate the effect of different standardizations of the generated database, the optimal definition of KNN configuration parameters, and the selection of features that maximize KNN performance. The results indicated that KNN trained with data that was standardized per cerebral hemisphere and per patient presented the best performance, achieving an accuracy of 94.35% (p < 0.001). By using feature selection algorithms, it was possible to achieve 93.5% in accuracy in selecting a subset of six features, improving computation time while processing in real time.

Root traits of European Vicia faba cultivars-Using machine learning to explore adaptations to agroclimatic conditions.

Faba bean (Vicia faba L.) is an important source of protein, but breeding for increased yield stability and stress tolerance is hampered by the scarcity of phenotyping information. Because comparisons of cultivars adapted to different agroclimatic zones improve our understanding of stress tolerance mechanisms, the root architecture and morphology of 16 European faba bean cultivars were studied at maturity. Different machine learning (ML) approaches were tested in their usefulness to analyse trait variations between cultivars. A supervised, that is, hypothesis-driven, ML approach revealed that cultivars from Portugal feature greater and coarser but less frequent lateral roots at the top of the taproot, potentially enhancing water uptake from deeper soil horizons. Unsupervised clustering revealed that trait differences between northern and southern cultivars are not predominant but that two cultivar groups, independently from major and minor types, differ largely in overall root system size. Methodological guidelines on how to use powerful ML methods such as random forest models for enhancing the phenotypical exploration of plants are given.

Automated identification of Monogeneans using digital image processing and K-nearest neighbour approaches.

BACKGROUND: Monogeneans are flatworms (Platyhelminthes) that are primarily found on gills and skin of fishes. Monogenean parasites have attachment appendages at their haptoral regions that help them to move about the body surface and feed on skin and gill debris. Haptoral attachment organs consist of sclerotized hard parts such as hooks, anchors and marginal hooks. Monogenean species are differentiated based on their haptoral bars, anchors, marginal hooks, reproductive parts' (male and female copulatory organs) morphological characters and soft anatomical parts. The complex structure of these diagnostic organs and also their overlapping in microscopic digital images are impediments for developing fully automated identification system for monogeneans (LNCS 7666:256-263, 2012), (ISDA; 457-462, 2011), (J Zoolog Syst Evol Res 52(2): 95-99. 2013;). In this study images of hard parts of the haptoral organs such as bars and anchors are used to develop a fully automated identification technique for monogenean species identification by implementing image processing techniques and machine learning methods. RESULT: Images of four monogenean species namely Sinodiplectanotrema malayanus, Trianchoratus pahangensis, Metahaliotrema mizellei and Metahaliotrema sp. (undescribed) were used to develop an automated technique for identification. K-nearest neighbour (KNN) was applied to classify the monogenean specimens based on the extracted features. 50% of the dataset was used for training and the other 50% was used as testing for system evaluation. Our approach demonstrated overall classification accuracy of 90%. In this study Leave One Out (LOO) cross validation is used for validation of our system and the accuracy is 91.25%. CONCLUSIONS: The methods presented in this study facilitate fast and accurate fully automated classification of monogeneans at the species level. In future studies more classes will be included in the model, the time to capture the monogenean images will be reduced and improvements in extraction and selection of features will be implemented.

Fault Detection Using the Clustering-kNN Rule for Gas Sensor Arrays.

The k-nearest neighbour (kNN) rule, which naturally handles the possible non-linearity of data, is introduced to solve the fault detection problem of gas sensor arrays. In traditional fault detection methods based on the kNN rule, the detection process of each new test sample involves all samples in the entire training sample set. Therefore, these methods can be computation intensive in monitoring processes with a large volume of variables and training samples and may be impossible for real-time monitoring. To address this problem, a novel clustering-kNN rule is presented. The landmark-based spectral clustering (LSC) algorithm, which has low computational complexity, is employed to divide the entire training sample set into several clusters. Further, the kNN rule is only conducted in the cluster that is nearest to the test sample; thus, the efficiency of the fault detection methods can be enhanced by reducing the number of training samples involved in the detection process of each test sample. The performance of the proposed clustering-kNN rule is fully verified in numerical simulations with both linear and non-linear models and a real gas sensor array experimental system with different kinds of faults. The results of simulations and experiments demonstrate that the clustering-kNN rule can greatly enhance both the accuracy and efficiency of fault detection methods and provide an excellent solution to reliable and real-time monitoring of gas sensor arrays.

Missing value imputation strategies for metabolomics data.

The origin of missing values can be caused by different reasons and depending on these origins missing values should be considered differently and dealt with in different ways. In this research, four methods of imputation have been compared with respect to revealing their effects on the normality and variance of data, on statistical significance and on the approximation of a suitable threshold to accept missing data as truly missing. Additionally, the effects of different strategies for controlling familywise error rate or false discovery and how they work with the different strategies for missing value imputation have been evaluated. Missing values were found to affect normality and variance of data and k-means nearest neighbour imputation was the best method tested for restoring this. Bonferroni correction was the best method for maximizing true positives and minimizing false positives and it was observed that as low as 40% missing data could be truly missing. The range between 40 and 70% missing values was defined as a "gray area" and therefore a strategy has been proposed that provides a balance between the optimal imputation strategy that was k-means nearest neighbor and the best approximation of positioning real zeros.

Detection and mitigation of coordinated cyber-physical attack in CPPS.

Cyber-Physical Power System (CPPS) refers to a system in which the elements of the internet and the physical power system communicate and work together. With the use of modern communication and information technology, grid monitoring and control have improved. However, the components of a cyber system are extremely vulnerable to cyberattacks via cyber connections due to inadequate cyber security measures. Therefore, an adaptive defence strategy is required for the analysis and mitigation of the coordinated attack. The conventional approach of using an offline controller requires tuning for changes in the operating conditions of the system, which is inappropriate for the modern CPPS. To counter the coordinated attack, a framework that integrates STATCOM based Adaptive Model Predictive Controller with RPME and time delay compensator is proposed. This paper addresses attack impact, detection, and mitigation methods in CPPS. In both time domain and frequency domain simulations the case studies are conducted for three distinct situations namely physical attack, cyberattack, and coordinated attack. Convolutional Neural Network (CNN), Support Vector Machine (SVM), Random Forest (RF), and K Nearest Neighbour (KNN) are four data-driven methods used for the detection of anomalies in PMU measurement data. Simulation studies show that CNN performs better in anomaly detection than other classifiers based on assessed performance metrics. For coordinated attack mitigation the proposed STATCOM based Adaptive Model Predictive Controller with RPME quickly recovers the system than the STATCOM based conventional lead-lag controller. The efficacy of the proposed strategy is validated on the WSCC 3 machine 9 bus system.

A novel deep learning approach for sickle cell anemia detection in human RBCs using an improved wrapper-based feature selection technique in microscopic blood smear images.

Sickle Cell Anemia (SCA) is a disorder in Red Blood Cells (RBCs) of human blood. Children under five years and pregnant women are mostly affected by SCA. Early diagnosis of this ailment can save lives. In recent years, the computer aided diagnosis of SCA is preferred to resolve this issue. A novel and effective deep learning approach for identification of sickle cell anemia is proposed in this work. Around nine hundred microscopic images of human red blood cells are obtained from the public database 'erythrocytes IDB'. All the images are resized uniformly. About 2048 deep features are extracted from the fully connected layer of pre-trained model InceptionV3. These features are further subjected to classification using optimization-based methods. An improved wrapper-based feature selection technique is implemented using Multi- Objective Binary Grey Wolf Optimization (MO-BGWO) approach with KNN and SVM for classification. The detection of sickle cell is also performed using typical InceptionV3 model by using SoftMax layer. It is observed that the performance of the proposed system seems to be high when compared to the classification using the original InceptionV3 model. The results are validated by various evaluation metrics such as accuracy, precision, sensitivity, specificity and F1-score. The SVM classifier yields high accuracy of about 96%. The optimal subset of deep features along with SVM enhances the system performance in the proposed work. Thus, the proposed approach is appropriate for pathologists to take early clinical decisions on detection of sickle cells.

Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel.

Accurate prediction of heart failure can help prevent life-threatening situations. Several factors contribute to the risk of heart failure, including underlying heart diseases such as coronary artery disease or heart attack, diabetes, hypertension, obesity, certain medications, and lifestyle habits such as smoking and excessive alcohol intake. Machine learning approaches to predict and detect heart disease hold significant potential for clinical utility but face several challenges in their development and implementation. This research proposes a machine learning metamodel for predicting a patient's heart failure based on clinical test data. The proposed metamodel was developed based on Random Forest Classifier, Gaussian Naive Bayes, Decision Tree models, and k-Nearest Neighbor as the final estimator. The metamodel is trained and tested utilizing a combined dataset comprising five well-known heart datasets (Statlog Heart, Cleveland, Hungarian, Switzerland, and Long Beach), all sharing 11 standard features. The study shows that the proposed metamodel can predict heart failure more accurately than other machine learning models, with an accuracy of 87%.

Combining seeded region growing and k-nearest neighbours for the segmentation of routinely acquired spatio-temporal image data.

PURPOSE: The acquisition conditions of medical imaging are often precisely defined, leading to a high homogeneity among different data sets. Nonetheless, outliers or artefacts still appear and need to be reliably detected to ensure a reliable diagnosis. Thus, the algorithms need to handle small sample sizes especially, when working with domain specific imaging modalities. METHODS: In this work, we suggest a pipeline for the detection and segmentation of light pollution in near-infrared fluorescence optical imaging (NIR-FOI), based on a small sample size. NIR-FOI produces spatio-temporal data with two spatial and one temporal dimension. To calculate a two-dimensional light pollution map for the entire image stack, we combine region growing and k-nearest neighbours (kNN), which classifies pixels into fore- and background by its entire temporal component. Thus, decision-making on reduced data is omitted. RESULTS: We achieved a [Formula: see text] score of 0.99 for classifying a data set as light polluted or pollution-free. Additionally, we reached a total [Formula: see text] score of 0.90 for detecting regions of interest within the polluted data sets. Finally, an average Dice's coefficient measuring the segmentation performance over all polluted data sets of 0.80 was accomplished. CONCLUSIONS: A Dice's coefficient of 0.80 for the area segmentation does not seem perfect. However, there are two main factors, besides true prediction errors, lowering the score: Segmentation mistakes on small areas lead to a rapid decrease in the score and labelling errors due to complex data. However, in combination with the light-polluted data set and pollution area detection, these results can be considered successful and play a key role in our general goal: Exploiting NIR-FOI for the early detection of arthritis within hand joints.

Diabetes prediction using machine learning and explainable AI techniques.

Globally, diabetes affects 537 million people, making it the deadliest and the most common non-communicable disease. Many factors can cause a person to get affected by diabetes, like excessive body weight, abnormal cholesterol level, family history, physical inactivity, bad food habit etc. Increased urination is one of the most common symptoms of this disease. People with diabetes for a long time can get several complications like heart disorder, kidney disease, nerve damage, diabetic retinopathy etc. But its risk can be reduced if it is predicted early. In this paper, an automatic diabetes prediction system has been developed using a private dataset of female patients in Bangladesh and various machine learning techniques. The authors used the Pima Indian diabetes dataset and collected additional samples from 203 individuals from a local textile factory in Bangladesh. Feature selection algorithm mutual information has been applied in this work. A semi-supervised model with extreme gradient boosting has been utilized to predict the insulin features of the private dataset. SMOTE and ADASYN approaches have been employed to manage the class imbalance problem. The authors used machine learning classification methods, that is, decision tree, SVM, Random Forest, Logistic Regression, KNN, and various ensemble techniques, to determine which algorithm produces the best prediction results. After training on and testing all the classification models, the proposed system provided the best result in the XGBoost classifier with the ADASYN approach with 81% accuracy, 0.81 F1 coefficient and AUC of 0.84. Furthermore, the domain adaptation method has been implemented to demonstrate the versatility of the proposed system. The explainable AI approach with LIME and SHAP frameworks is implemented to understand how the model predicts the final results. Finally, a website framework and an Android smartphone application have been developed to input various features and predict diabetes instantaneously. The private dataset of female Bangladeshi patients and programming codes are available at the following link: https://github.com/tansin-nabil/Diabetes-Prediction-Using-Machine-Learning.

Privacy-preserving k-NN interpolation over two encrypted databases.

Cloud computing enables users to outsource their databases and the computing functionalities to a cloud service provider to avoid the cost of maintaining a private storage and computational requirements. It also provides universal access to data, applications, and services without location dependency. While cloud computing provides many benefits, it possesses a number of security and privacy concerns. Outsourcing data to a cloud service provider in encrypted form may help to overcome these concerns. However, dealing with the encrypted data makes it difficult for the cloud service providers to perform some operations over the data that will especially be required in query processing tasks. Among the techniques employed in query processing task, the k-nearest neighbor method draws attention due to its simplicity and efficiency, particularly on massive data sets. A number of k-nearest neighbor algorithms for query processing task on a single encrypted database have been proposed. However, the performance of k-nearest neighbor algorithms on a single database may create accuracy and reliability problems. It is a fact that collaboration among different cloud service providers yields more accurate and more reliable results in query processing. By considering this fact, we focus on the k-nearest neighbor (k-NN) problem over two encrypted databases. We introduce a secure two-party k-NN interpolation protocol that enables a query owner to extract the interpolation of the k-nearest neighbors of a query point from two different databases outsourced to two different cloud service providers. We also show that our protocol protects the confidentiality of the data and the query point, and hides data access patterns. Furthermore, we conducted a number of experiment to demonstrate the efficiency of our protocol. The results show that the running time of our protocol is linearly dependent on both the number of nearest neighbours and data size.