Cardiac disease risk prediction using machine learning algorithms.

Heart attack is a life-threatening condition which is mostly caused due to coronary disease resulting in death in human beings. Detecting the risk of heart diseases is one of the most important problems in medical science that can be prevented and treated with early detection and appropriate medical management; it can also help to predict a large number of medical needs and reduce expenses for treatment. Predicting the occurrence of heart diseases by machine learning (ML) algorithms has become significant work in healthcare industry. This study aims to create a such system that is used for predicting whether a patient is likely to develop heart attacks, by analysing various data sources including electronic health records and clinical diagnosis reports from hospital clinics. ML is used as a process in which computers learn from data in order to make predictions about new datasets. The algorithms created for predictive data analysis are often used for commercial purposes. This paper presents an overview to forecast the likelihood of a heart attack for which many ML methodologies and techniques are applied. In order to improve medical diagnosis, the paper compares various algorithms such as Random Forest, Regression models, K-nearest neighbour imputation (KNN), Naïve Bayes algorithm etc. It is found that the Random Forest algorithm provides a better accuracy of 88.52% in forecasting heart attack risk, which could herald a revolution in the diagnosis and treatment of cardiovascular illnesses.

K-nearest neighbor based on exploratory data analysis of curriculum models of Chinese early childhood education.

This research examines the unique Chinese approaches to implementing the Early Childhood Curriculum (ECC) in Shenzhen and Hong Kong, drawing on School-based Curriculum Development (SBCD) studies. A total of 200 administrators and teachers were interviewed in total, and transcripts from those interviews were examined, cross-checked, and assessed using document analysis and classroom observation. Through interviews that have been conducted by administrators and teachers analyzed by document analysis and classroom observation, the influence of Chinese culture on ECC implementation is explored using the Cultural-Historical Activity Theory (CHAT). An exploratory, inferential, and descriptive statistical approach evaluates the sociocultural mechanism of ECC in Chinese society. The proposed framework utilizes K-Nearest Neighbor (KNN) regression analysis to illustrate how social development leads to cultural fusion and conflicts. The overall sociocultural framework promotes cultural growth and inheritance in China's early childhood education settings.

Artificial Intelligence (AI) and Nuclear Features from the Fine Needle Aspirated (FNA) Tissue Samples to Recognize Breast Cancer.

Breast cancer is one of the paramount causes of new cancer cases worldwide annually. It is a malignant neoplasm that develops in the breast cells. The early screening of this disease is essential to prevent its metastasis. A mammogram X-ray image is the most common screening tool practiced currently when this disease is suspected; all the breast lesions identified are not malignant. The invasive fine needle aspiration (FNA) of a breast mass sample is the secondary screening tool to clinically examine cancerous lesions. The visual image analysis of the stained aspirated sample imposes a challenge for the cytologist to identify the malignant cells accurately. The formulation of an artificial intelligence-based objective technique on top of the introspective assessment is essential to avoid misdiagnosis. This paper addresses several artificial intelligence (AI)-based techniques to diagnose breast cancer from the nuclear features of FNA samples. The Wisconsin Breast Cancer dataset (WBCD) from the UCI machine learning repository is applied for this investigation. Significant statistical parameters are measured to evaluate the performance of the proposed techniques. The best detection accuracy of 98.10% is achieved with a two-layer feed-forward neural network (FFNN). Finally, the developed algorithm's performance is compared with some state-of-the-art works in the literature.

Assessment of 'Golden Delicious' Apples Using an Electronic Nose and Machine Learning to Determine Ripening Stages.

Consumers often face a lack of information regarding the quality of apples available in supermarkets. General appearance factors, such as color, mechanical damage, or microbial attack, influence consumer decisions on whether to purchase or reject the apples. Recently, devices known as electronic noses provide an easy-to-use and non-destructive assessment of ripening stages based on Volatile Organic Compounds (VOCs) emitted by the fruit. In this study, the 'Golden Delicious' apples, stored and monitored at the ambient temperature, were analyzed in the years 2022 and 2023 to collect data from four Metal Oxide Semiconductor (MOS) sensors (MQ3, MQ135, MQ136, and MQ138). Three ripening stages (less ripe, ripe, and overripe) were identified using Principal Component Analysis (PCA) and the K-means clustering approach from various datasets based on sensor measurements in four experiments. After applying the K-Nearest Neighbors (KNN) model, the results showed successful classification of apples for specific datasets, achieving an accuracy higher than 75%. For the dataset with measurements from all experiments, an impressive accuracy of 100% was achieved on specific test sets and on the evaluation set from new, completely independent experiments. Additionally, correlation and PCA analysis showed that choosing two or three sensors can provide equally successful results. Overall, the e-nose results highlight the importance of analyzing data from several experiments performed over a longer period after the harvest of apples. There are similarities and differences in investigated VOC parameters (ethylene, esters, alcohols, and aldehydes) for less or more mature apples analyzed during autumn or spring, which can improve the determination of the ripening stage with higher predicting success for apples investigated in the spring.

Shirt-color recognition for the color-blindness.

Color-blind is a generic disability whereby the affected individuals are not given the opportunity to benefit from the various functions provided by color that would impact humans physically and psychologically. Although this disability is not fatal, it brought plenty of turbulence in the affected individuals' daily activities. This paper aims to develop a system for recognizing and detecting colors of clothes in images, improve accuracy by using advanced algorithms to handle lighting variations, and provide color matching recommendations to assist color-blind individuals in making informed choices when purchasing shirts. The proposed methodology for color recognition involves:•retrieving the RGB values of a given point from the input image and converting them into HSV values.•creating web application integrated with a machine learning model to classify and predict the corresponding color based on the HSV values.•predicting the color name with suggestions of matching colors will be displayed on the interface.

Prediction of segmental motor outcomes in traumatic spinal cord injury: Advances beyond sum scores.

BACKGROUND AND OBJECTIVES: Neurological and functional recovery after traumatic spinal cord injury (SCI) is highly challenged by the level of the lesion and the high heterogeneity in severity (different degrees of in/complete SCI) and spinal cord syndromes (hemi-, ant-, central-, and posterior cord). So far outcome predictions in clinical trials are limited in targeting sum motor scores of the upper (UEMS) and lower limb (LEMS) while neglecting that the distribution of motor function is essential for functional outcomes. The development of data-driven prediction models of detailed segmental motor recovery for all spinal segments from the level of lesion towards the lowest motor segments will improve the design of rehabilitation programs and the sensitivity of clinical trials. METHODS: This study used acute-phase International Standards for Neurological Classification of SCI exams to forecast 6-month recovery of segmental motor scores as the primary evaluation endpoint. Secondary endpoints included severity grade improvement, independent walking, and self-care ability. Different similarity metrics were explored for k-nearest neighbor (kNN) matching within 1267 patients from the European Multicenter Study about Spinal Cord Injury before validation in 411 patients from the Sygen trial. The kNN performance was compared to linear and logistic regression models. RESULTS: We obtained a population-wide root-mean-squared error (RMSE) in motor score sequence of 0.76(0.14, 2.77) and competitive functional score predictions (AUCwalker = 0.92, AUCself-carer = 0.83) for the kNN algorithm, improving beyond the linear regression task (RMSElinear = 0.98(0.22, 2.57)). The validation cohort showed comparable results (RMSE = 0.75(0.13, 2.57), AUCwalker = 0.92). We deploy the final historic control model as a web tool for easy user interaction (https://hicsci.ethz.ch/). DISCUSSION: Our approach is the first to provide predictions across all motor segments independent of the level and severity of SCI. We provide a machine learning concept that is highly interpretable, i.e. the prediction formation process is transparent, that has been validated across European and American data sets, and provides reliable and validated algorithms to incorporate external control data to increase sensitivity and feasibility of multinational clinical trials.

Rolling Bearing Fault Diagnosis Based on SABO-VMD and WMH-KNN.

To improve the performance of roller bearing fault diagnosis, this paper proposes an algorithm based on subtraction average-based optimizer (SABO), variational mode decomposition (VMD), and weighted Manhattan-K nearest neighbor (WMH-KNN). Initially, the SABO algorithm uses a composite objective function, including permutation entropy and mutual information entropy, to optimize the input parameters of VMD. Subsequently, the optimized VMD is used to decompose the signal to obtain the optimal decomposition characteristics and the corresponding intrinsic mode function (IMF). Finally, the weighted Manhattan function (WMH) is used to enhance the classification distance of the KNN algorithm, and WMH-KNN is used for fault diagnosis based on the optimized IMF features. The performance of the SABO-VMD and WMH-KNN models is verified through two experimental cases and compared with traditional methods. The results show that the accuracy of motor-bearing fault diagnosis is significantly improved, reaching 97.22% in Dataset 1, 98.33% in Dataset 2, and 99.2% in Dataset 3. Compared with traditional methods, the proposed method significantly reduces the false positive rate.

Benchmarking quantum versions of the kNN algorithm with a metric based on amplitude-encoded features.

This work introduces a quantum subroutine for computing the distance between two patterns and integrates it into two quantum versions of the kNN classifier algorithm: one proposed by Schuld et al. and the other proposed by Quezada et al. Notably, our proposed subroutine is tailored to be memory-efficient, requiring fewer qubits for data encoding, while maintaining the overall complexity for both QkNN versions. This research focuses on comparing the performance of the two quantum kNN algorithms using the original Hamming distance with qubit-encoded features and our proposed subroutine, which computes the distance using amplitude-encoded features. Results obtained from analyzing thirteen different datasets (Iris, Seeds, Raisin, Mine, Cryotherapy, Data Bank Authentication, Caesarian, Wine, Haberman, Transfusion, Immunotherapy, Balance Scale, and Glass) show that both algorithms benefit from the proposed subroutine, achieving at least a 50% reduction in the number of required qubits, while maintaining a similar overall performance. For Shuld's algorithm, the performance improved in Cryotherapy (68.89% accuracy compared to 64.44%) and Balance Scale (85.33% F1 score compared to 78.89%), was worse in Iris (86.0% accuracy compared to 95.33%) and Raisin (77.67% accuracy compared to 81.56%), and remained similar in the remaining nine datasets. While for Quezada's algorithm, the performance improved in Caesarian (68.89% F1 score compared to 58.22%), Haberman (69.94% F1 score compared to 62.31%) and Immunotherapy (76.88% F1 score compared to 69.67%), was worse in Iris (82.67% accuracy compared to 95.33%), Balance Scale (77.97% F1 score compared to 69.21%) and Glass (40.04% F1 score compared to 28.79%), and remained similar in the remaining seven datasets.

Colorectal cancer prognosis based on dietary pattern using synthetic minority oversampling technique with K-nearest neighbors approach.

Generally, a person's life span depends on their food consumption because it may cause deadly diseases like colorectal cancer (CRC). In 2020, colorectal cancer accounted for one million fatalities globally, representing 10% of all cancer casualties. 76,679 males and 78,213 females over the age of 59 from ten states in the United States participated in this analysis. During follow-up, 1378 men and 981 women were diagnosed with colon cancer. This prospective cohort study used 231 food items and their variants as input features to identify CRC patients. Before labelling any foods as colorectal cancer-causing foods, it is ethical to analyse facts like how many grams of food should be consumed daily and how many times a week. This research examines five classification algorithms on real-time datasets: K-Nearest Neighbour (KNN), Decision Tree (DT), Random Forest (RF), Logistic Regression with Classifier Chain (LRCC), and Logistic Regression with Label Powerset (LRLC). Then, the SMOTE algorithm is applied to deal with and identify imbalances in the data. Our study shows that eating more than 10 g/d of low-fat butter in bread (RR 1.99, CI 0.91-4.39) and more than twice a week (RR 1.49, CI 0.93-2.38) increases CRC risk. Concerning beef, eating in excess of 74 g of beef steak daily (RR 0.88, CI 0.50-1.55) and having it more than once a week (RR 0.88, CI 0.62-1.23) decreases the risk of CRC, respectively. While eating beef and dairy products in a daily diet should be cautious about quantity. Consuming those items in moderation on a regular basis will protect us against CRC risk. Meanwhile, a high intake of poultry (RR 0.2, CI 0.05-0.81), fish (RR 0.82, CI 0.31-2.16), and pork (RR 0.67, CI 0.17-2.65) consumption negatively correlates to CRC hazards.

Enhanced Temperature Stability of Pyroelectric Sensing in Multilayer Potassium Sodium Niobate-Based Ceramics with Graded Polarization Rotation.

Pyroelectric effect which refers to electrical responses induced by time temperature-dependent fluctuations has received extensive attention, showing promising application prospects for infrared (IR) technology. Although enhanced pyroelectric performances are obtained in potassium sodium niobate-based ceramics at room temperature via multi-symmetries coexistence design, the poor pyroelectric temperature stability is still an urging desire that needs to be resolved. Herin, by constructing multilayer composite ceramics and adjusting the proportion of stacked layers, improved pyroelectric coefficient, and figures of merit (FOMs), as well as enhanced temperature stabilities can be achieved. With a remained high pyroelectric coefficient of 5.45 × 10-4 C m-2°C-1 at room temperature, the pyroelectric parameters almost keep unchanged in the temperature range of 30-100 °C, showing great properties advantages compared with previous reports. The excellent properties can be attributed to the graded polarization rotation states among each lamination induced by successive phase transitions. The novel strategy for achieving stable pyroelectric sensing can further promote the application in the IR sensors field.

Impact of climate change on future flood susceptibility projections under shared socioeconomic pathway scenarios in South Asia using artificial intelligence algorithms.

This study investigated the impact of climate change on flood susceptibility in six South Asian countries Afghanistan, Bangladesh, Bhutan, Bharat (India), Nepal, and Pakistan-under two distinct Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6 and SSP5-5.8, for 2041-2060 and 2081-2100. To predict flood susceptibility, we employed three artificial intelligence (AI) algorithms: the K-nearest neighbor (KNN), conditional inference random forest (CIRF), and regularized random forest (RRF). Predictions were based on data from 2452 historical flood events, alongside climatic variables measured over monthly, seasonal, and annual timeframes. The innovative aspect of this research is the emphasis on using climatic variables across these progressively condensed timeframes, specifically addressing eight precipitation factors. The performance evaluation, employing the area under the receiver operating characteristic curve (AUC) metric, identified the RRF model as the most accurate, with the highest AUC of 0.94 during the testing phase, followed by the CIRF (AUC = 0.91) and the KNN (AUC = 0.86). An analysis of variable importance highlighted the substantial role of certain climatic factors, namely precipitation in the warmest quarter, annual precipitation, and precipitation during the wettest month, in the modeling of flood susceptibility in South Asia. The resultant flood susceptibility maps demonstrated the influence of climate change scenarios on susceptibility classifications, signalling a dynamic landscape of flood-prone areas over time. The findings revealed variable trends under different climate change scenarios and periods, with marked differences in the percentage of areas classified as having high and very high flood susceptibility. Overall, this study advances our understanding of how climate change affects flood susceptibility in South Asia and offers an essential tool for assessing and managing flood risks in the region.

A Delayed Spiking Neural Membrane System for Adaptive Nearest Neighbor-Based Density Peak Clustering.

Although the density peak clustering (DPC) algorithm can effectively distribute samples and quickly identify noise points, it lacks adaptability and cannot consider the local data structure. In addition, clustering algorithms generally suffer from high time complexity. Prior research suggests that clustering algorithms grounded in P systems can mitigate time complexity concerns. Within the realm of membrane systems (P systems), spiking neural P systems (SN P systems), inspired by biological nervous systems, are third-generation neural networks that possess intricate structures and offer substantial parallelism advantages. Thus, this study first improved the DPC by introducing the maximum nearest neighbor distance and K-nearest neighbors (KNN). Moreover, a method based on delayed spiking neural P systems (DSN P systems) was proposed to improve the performance of the algorithm. Subsequently, the DSNP-ANDPC algorithm was proposed. The effectiveness of DSNP-ANDPC was evaluated through comprehensive evaluations across four synthetic datasets and 10 real-world datasets. The proposed method outperformed the other comparison methods in most cases.

Deep fine-KNN classification of ovarian cancer subtypes using efficientNet-B0 extracted features: a comprehensive analysis.

This study presents a robust approach for the classification of ovarian cancer subtypes through the integration of deep learning and k-nearest neighbor (KNN) methods. The proposed model leverages the powerful feature extraction capabilities of EfficientNet-B0, utilizing its deep features for subsequent fine-grained classification using the fine-KNN approach. The UBC-OCEAN dataset, encompassing histopathological images of five distinct ovarian cancer subtypes, namely, high-grade serous carcinoma (HGSC), clear-cell ovarian carcinoma (CC), endometrioid carcinoma (EC), low-grade serous carcinoma (LGSC), and mucinous carcinoma (MC), served as the foundation for our investigation. With a dataset comprising 725 images, divided into 80% for training and 20% for testing, our model exhibits exceptional performance. Both the validation and testing phases achieved 100% accuracy, underscoring the efficacy of the proposed methodology. In addition, the area under the curve (AUC), a key metric for evaluating the model's discriminative ability, demonstrated high performance across various subtypes, with AUC values of 0.94, 0.78, 0.69, 0.92, and 0.94 for MC. Furthermore, the positive likelihood ratios (LR+) were indicative of the model's diagnostic utility, with notable values for each subtype: CC (27.294), EC (9.441), HGSC (12.588), LGSC (17.942), and MC (17.942). These findings demonstrate the effectiveness of the model in distinguishing between ovarian cancer subtypes, positioning it as a promising tool for diagnostic applications. The demonstrated accuracy, AUC values, and LR+ values underscore the potential of the model as a valuable diagnostic tool, contributing to the advancement of precision medicine in the field of ovarian cancer research.

Hybrid multimodal fusion for graph learning in disease prediction.

Graph neural networks (GNNs) have gained significant attention in disease prediction where the latent embeddings of patients are modeled as nodes and the similarities among patients are represented through edges. The graph structure, which determines how information is aggregated and propagated, plays a crucial role in graph learning. Recent approaches typically create graphs based on patients' latent embeddings, which may not accurately reflect their real-world closeness. Our analysis reveals that raw data, such as demographic attributes and laboratory results, offers a wealth of information for assessing patient similarities and can serve as a compensatory measure for graphs constructed exclusively from latent embeddings. In this study, we first construct adaptive graphs from both latent representations and raw data respectively, and then merge these graphs via weighted summation. Given that the graphs may contain extraneous and noisy connections, we apply degree-sensitive edge pruning and kNN sparsification techniques to selectively sparsify and prune these edges. We conducted intensive experiments on two diagnostic prediction datasets, and the results demonstrate that our proposed method surpasses current state-of-the-art techniques.

Development of a multi-objective reservoir operation model for water quality-quantity management.

This study aims to develop a multi-objective quantitative-qualitative reservoir operation model (MOQQROM) by a simulation-optimization approach. However, the main challenge of these models is their computational complexity. The simulation-optimization method used in this study consists of CE-QUAL-W2 as a hydrodynamic and water quality simulation model and a multi-objective firefly algorithm-k nearest neighbor (MOFA-KNN) as an optimization algorithm which is an efficient algorithm to overcome the computational burden in simulation-optimization approaches by decreasing simulation model calls. MOFA-KNN was expanded for this study, and its performance was evaluated in the MOQQROM. Three objectives were considered in this study, including (1) the sum of the squared mass of total dissolved solids (TDS), (2) the sum of the squared temperature difference between reservoir inflow and outflow as water quality objectives, and (3) the vulnerability index as a water quantity objective. Aidoghmoush reservoir was employed as a case study, and the model was investigated under three scenarios, including the normal, wet, and dry years. Results showed the expanded MOFA-KNN reduced the number of original simulation model calls compared to the total number of simulations in MOQQROM by more than 99%, indicating its efficacy in significantly reducing execution time. The three most desired operating policies for meeting each objective were selected for investigation. Results showed that the operation policy with the best value for the second objective could be chosen as a compromise policy to balance the two conflicting goals of improving quality and supplying the demand in normal and wet scenarios. In terms of contamination mass, this policy was, on average, 16% worse than the first policy and 40% better than the third policy in the normal scenario. In the wet scenario, it was, on average, 55% worse than the first policy and 16% better than the third policy. The outflow temperature of this policy was, on average, only 8.35% different from the inflow temperature in the normal scenario and 0.93% different in the wet scenario. The performance of the developed model is satisfactory.

A proposed hybrid model of ANN and KNN for solar cell defects detection and temperature prediction using fuzzy image segmentation.

This paper presents a novel hybrid model employing Artificial Neural Networks (ANN) and Mathematical Morphology (MM) for the effective detection of defects in solar cells. Focusing on issues such as broken corners and black edges caused by environmental factors like broken glass cover, dust, and temperature variations. This study utilizes a hybrid model of ANN and K-Nearest Neighbor (KNN) for temperature prediction. This hybrid approach leverages the strengths of both models, potentially opening up new avenues for improved accuracy in temperature forecasting, which is critical for solar energy applications. The significance lies in the interconnectedness of temperature fluctuations and solar cell efficiency, leading to defects. The proposed model aims to predict temperatures accurately, providing insights into potential solar cell efficiency problems. Subsequently, this work studies the transitions to defect detection using Fuzzy C-Means (FCM) clustering and MM techniques. The hybrid model demonstrates accurate temperature prediction with Mean Absolute Percentage Error (MAPE) values of 0.92 %, 0.72 %, and 1.3 % for average, maximum, and minimum temperatures, respectively. The defect detection process yields a detection accuracy (CR) of 96 % and sensitivity of detection (SD) of 89 %. This work is validated compared to the literature work done and by using K-fold cross validation technique. The proposed work emphasizes the improvement in defect detection accuracy and the overall quality enhancement of solar cells.

A Neoteric Feature Extraction Technique to Predict the Survival of Gastric Cancer Patients.

BACKGROUND: At the time of cancer diagnosis, it is crucial to accurately classify malignant gastric tumors and the possibility that patients will survive. OBJECTIVE: This study aims to investigate the feasibility of identifying and applying a new feature extraction technique to predict the survival of gastric cancer patients. METHODS: A retrospective dataset including the computed tomography (CT) images of 135 patients was assembled. Among them, 68 patients survived longer than three years. Several sets of radiomics features were extracted and were incorporated into a machine learning model, and their classification performance was characterized. To improve the classification performance, we further extracted another 27 texture and roughness parameters with 2484 superficial and spatial features to propose a new feature pool. This new feature set was added into the machine learning model and its performance was analyzed. To determine the best model for our experiment, Random Forest (RF) classifier, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes (NB) (four of the most popular machine learning models) were utilized. The models were trained and tested using the five-fold cross-validation method. RESULTS: Using the area under ROC curve (AUC) as an evaluation index, the model that was generated using the new feature pool yields AUC = 0.98 ± 0.01, which was significantly higher than the models created using the traditional radiomics feature set (p < 0.04). RF classifier performed better than the other machine learning models. CONCLUSIONS: This study demonstrated that although radiomics features produced good classification performance, creating new feature sets significantly improved the model performance.

A machine learning approach to evaluate the impact of virtual balance/cognitive training on fall risk in older women.

Introduction: Novel technologies based on virtual reality (VR) are creating attractive virtual environments with high ecological value, used both in basic/clinical neuroscience and modern medical practice. The study aimed to evaluate the effects of VR-based training in an elderly population. Materials and methods: The study included 36 women over the age of 60, who were randomly divided into two groups subjected to balance-strength and balance-cognitive training. The research applied both conventional clinical tests, such as (a) the Timed Up and Go test, (b) the five-times sit-to-stand test, and (c) the posturographic exam with the Romberg test with eyes open and closed. Training in both groups was conducted for 10 sessions and embraced exercises on a bicycle ergometer and exercises using non-immersive VR created by the ActivLife platform. Machine learning methods with a k-nearest neighbors classifier, which are very effective and popular, were proposed to statistically evaluate the differences in training effects in the two groups. Results and conclusion: The study showed that training using VR brought beneficial improvement in clinical tests and changes in the pattern of posturographic trajectories were observed. An important finding of the research was a statistically significant reduction in the risk of falls in the study population. The use of virtual environments in exercise/training has great potential in promoting healthy aging and preventing balance loss and falls among seniors.

Deep feature extraction and fine κ-nearest neighbour for enhanced human papillomavirus detection in cervical cancer - a comprehensive analysis of colposcopy images.

Introduction: This study introduces a novel methodology for classifying human papillomavirus (HPV) using colposcopy images, focusing on its potential in diagnosing cervical cancer, the second most prevalent malignancy among women globally. Addressing a crucial gap in the literature, this study highlights the unexplored territory of HPV-based colposcopy image diagnosis for cervical cancer. Emphasising the suitability of colposcopy screening in underdeveloped and low-income regions owing to its small, cost-effective setup that eliminates the need for biopsy specimens, the methodological framework includes robust dataset augmentation and feature extraction using EfficientNetB0 architecture. Material and methods: The optimal convolutional neural network model was selected through experimentation with 19 architectures, and fine-tuning with the fine κ-nearest neighbour algorithm enhanced the classification precision, enabling detailed distinctions with a single neighbour. Results: The proposed methodology achieved outstanding results, with a validation accuracy of 99.9% and an area under the curve (AUC) of 99.86%, with robust performance on test data, 91.4% accuracy, and an AUC of 91.76%. These remarkable findings underscore the effectiveness of the integrated approach, which offers a highly accurate and reliable system for HPV classification.Conclusions: This research sets the stage for advancements in medical imaging applications, prompting future refinement and validation in diverse clinical settings.

Prediction analysis of TBI 24-h survival outcome based on machine learning.

Background: Traumatic brain injury (TBI) is the major reason for the death of young people and is well known for its high mortality and morbidity. This paper aim to predict the 24h survival of patients with TBI. Methods: A total of 1224 samples were involved in this analysis, and the clinical indicators involved included age, gender, blood pressure, MGAP and other fields, among which the target variable was "outcome", which was a binary variable. The methods mainly involved in this paper include data visualization analysis, single factor analysis, feature engineering analysis, random forest model (RF), K-Nearst Neighbors (KNN) model, and so on. Logistic regression model (LR) and deep neural network model (DNN). We will oversample the training set using the SMOTE method because of the very unbalanced labeling of the sample itself. Results: Although the accuracy of all models is very high, the recall rate is relatively low. The DNN model with the best performance only reaches 0.17, and the corresponding AUC is 0.80. After resampling, we find that the recall rate of positive samples of all models has increased a lot, but the AUC of some models has decreased. Finally, the optimal model is LR, whose positive sample recall rate is 0.67 and AUC is 0.82. Conclusion: Through resampling, we obtained that the best model is the RF model, whose recall rate and AUC are the best, and the AUC level is about 0.87, indicating that the accuracy performance of the model is still good.