Predicting dyslipidemia incidence: unleashing machine learning algorithms on Lifestyle Promotion Project data.

BACKGROUND: Dyslipidemia, characterized by variations in plasma lipid profiles, poses a global health threat linked to millions of deaths annually. OBJECTIVES: This study focuses on predicting dyslipidemia incidence using machine learning methods, addressing the crucial need for early identification and intervention. METHODS: The dataset, derived from the Lifestyle Promotion Project (LPP) in East Azerbaijan Province, Iran, undergoes a comprehensive preprocessing, merging, and null handling process. Target selection involves five distinct dyslipidemia-related variables. Normalization techniques and three feature selection algorithms are applied to enhance predictive modeling. RESULT: The study results underscore the potential of different machine learning algorithms, specifically multi-layer perceptron neural network (MLP), in reaching higher performance metrics such as accuracy, F1 score, sensitivity and specificity, among other machine learning methods. Among other algorithms, Random Forest also showed remarkable accuracies and outperformed K-Nearest Neighbors (KNN) in metrics like precision, recall, and F1 score. The study's emphasis on feature selection detected meaningful patterns among five target variables related to dyslipidemia, indicating fundamental shared unities among dyslipidemia-related factors. Features such as waist circumference, serum vitamin D, blood pressure, sex, age, diabetes, and physical activity related to dyslipidemia. CONCLUSION: These results cooperatively highlight the complex nature of dyslipidemia and its connections with numerous factors, strengthening the importance of applying machine learning methods to understand and predict its incidence precisely.

An enhanced Coati Optimization Algorithm for global optimization and feature selection in EEG emotion recognition.

Emotion recognition based on Electroencephalography (EEG) signals has garnered significant attention across diverse domains including healthcare, education, information sharing, and gaming, among others. Despite its potential, the absence of a standardized feature set poses a challenge in efficiently classifying various emotions. Addressing the issue of high dimensionality, this paper introduces an advanced variant of the Coati Optimization Algorithm (COA), called eCOA for global optimization and selecting the best subset of EEG features for emotion recognition. Specifically, COA suffers from local optima and imbalanced exploitation abilities as other metaheuristic methods. The proposed eCOA incorporates the COA and RUNge Kutta Optimizer (RUN) algorithms. The Scale Factor (SF) and Enhanced Solution Quality (ESQ) mechanism from RUN are applied to resolve the raised shortcomings of COA. The proposed eCOA algorithm has been extensively evaluated using the CEC'22 test suite and two EEG emotion recognition datasets, DEAP and DREAMER. Furthermore, the eCOA is applied for binary and multi-class classification of emotions in the dimensions of valence, arousal, and dominance using a multi-layer perceptron neural network (MLPNN). The experimental results revealed that the eCOA algorithm has more powerful search capabilities than the original COA and seven well-known counterpart methods related to statistical, convergence, and diversity measures. Furthermore, eCOA can efficiently support feature selection to find the best EEG features to maximize performance on four quadratic emotion classification problems compared to the methods of its counterparts. The suggested method obtains a classification accuracy of 85.17% and 95.21% in the binary classification of low and high arousal emotions in two public datasets: DEAP and DREAMER, respectively, which are 5.58% and 8.98% superior to existing approaches working on the same datasets for different subjects, respectively.

Improvement of spatial estimation for soil organic carbon stocks in Yuksekova plain using Sentinel 2 imagery and gradient descent-boosted regression tree.

Carbon sequestration in earth surface is higher than the atmosphere, and the amount of carbon stored in wetlands is much greater than all other land surfaces. The purpose of this study was to estimate soil organic carbon stocks (SOCS) and investigate spatial distribution pattern of Yuksekova wetlands and surrounding lands in Hakkari province of Turkey using machine learning and remote sensing data. Disturbed and undisturbed soil samples were collected from 10-cm depth in 50 locations differed with land use and land cover. Vegetation, soil, and moisture indices were calculated using Sentinel 2 Multispectral Sensor Instrument (MSI) data. Significant correlations (p≤0.01) were obtained between the indices and SOCS; thus, the remote sensing indices (ARVI 0.43, BI -0.43, GSI -0.39, GNDI 0.44, NDVI 0.44, NDWI 0.38, and SRCI 0.51) were used as covariates in multi-layer perceptron neural network (MLP) and gradient descent-boosted regression tree (GBDT) machine learning models. Mean absolute error, root mean square error, and mean absolute percentage error were 3.94 (Mg C ha -1), 6.64 (Mg C ha-1), and 9.97%, respectively. The simple ratio clay index (SRCI), which represents the soil texture, was the most important factor in the SOCS estimation variance. In addition, the relationship between SRCI and Topsoil Grain Size Index revealed that topsoil clay content is a highly important parameter in spatial variation of SOCS. The spatial SOCS values obtained using the GBDT model and the mean SOCS values of the CORINE land cover classes were significantly different. The land cover has a significant effect on SOC in Yuksekova plain. The mean SOCS for continuously ponded fields was 45.58 Mg C ha-1, which was significantly different from the mean SOCS of arable lands. The mean SOCS in arable lands, with significant areas of natural vegetation, was 50.22 Mg C ha-1 and this amount was significantly higher from the SOCS of other land covers (p<0.01). The wetlands had the highest SOCS (61.46 Mg C ha-1), followed by the lands principally occupied by natural vegetation and used as rangelands around the wetland (50.22 Mg C ha-1). Environmental conditions had significant effect on SOCS in the study area. The use of remote sensing indices instead of using single bands as estimators in the GBDT algorithm minimized radiometric errors, and reliable spatial SOCS information was obtained by using the estimators. Therefore, the spatial estimation of SOCS can be successfully determined with up-to-date machine learning algorithms only using remote sensing predictor variables. Reliable estimation of SOCS in wetlands and surrounding lands can help understand policy and decision makers the importance of wetlands in mitigating the negative impacts of global warming.

Developing a Tuned Three-Layer Perceptron Fed with Trained Deep Convolutional Neural Networks for Cervical Cancer Diagnosis.

Cervical cancer is one of the most common types of cancer among women, which has higher death-rate than many other cancer types. The most common way to diagnose cervical cancer is to analyze images of cervical cells, which is performed using Pap smear imaging test. Early and accurate diagnosis can save the lives of many patients and increase the chance of success of treatment methods. Until now, various methods have been proposed to diagnose cervical cancer based on the analysis of Pap smear images. Most of the existing methods can be divided into two groups of methods based on deep learning techniques or machine learning algorithms. In this study, a combination method is presented, whose overall structure is based on a machine learning strategy, where the feature extraction stage is completely separate from the classification stage. However, in the feature extraction stage, deep networks are used. In this paper, a multi-layer perceptron (MLP) neural network fed with deep features is presented. The number of hidden layer neurons is tuned based on four innovative ideas. Additionally, ResNet-34, ResNet-50 and VGG-19 deep networks have been used to feed MLP. In the presented method, the layers related to the classification phase are removed in these two CNN networks, and the outputs feed the MLP after passing through a flatten layer. In order to improve performance, both CNNs are trained on related images using the Adam optimizer. The proposed method has been evaluated on the Herlev benchmark database and has provided 99.23 percent accuracy for the two-classes case and 97.65 percent accuracy for the 7-classes case. The results have shown that the presented method has provided higher accuracy than the baseline networks and many existing methods.

Prediction of urban expansion by using land cover change detection approach.

Bangladesh has been experiencing rapid urban expansion over the last few decades, contributing much to the region's land cover transition into the urban area. The study aims to employ geospatial modeling techniques to investigate land cover scenarios in the Pabna municipality of Bangladesh. Therefore, the research examined Cellular Automata Markov and Multi-Layer Perceptron Markov models to detect land cover for 2023 and 2028. The study selected the Multi-Layer Perceptron Markov as the best fit model over Cellular Automata Markov based on the highest kappa value. The result reveals that urban area has increased from 3.39 to 8.79 km2 over 1998-2018. Urban expansion and its surrounding area are primarily occurring towards the northeast directions. However, the extent of urban build-up land will grow from 3.39 km2 in 1998 to 11.01 km2 in 2023 and 12.44 km2 in 2028. Moreover, the future land cover map delineated that the urban growth will expand in the northeast part of the study area. The scenario shown in this paper would assist urban planners in quantifying the urban growth under different land cover features and thus preparing proper strategic measures.

Bispectral Analysis of Heart Rate Variability to Characterize and Help Diagnose Pediatric Sleep Apnea.

Pediatric obstructive sleep apnea (OSA) is a breathing disorder that alters heart rate variability (HRV) dynamics during sleep. HRV in children is commonly assessed through conventional spectral analysis. However, bispectral analysis provides both linearity and stationarity information and has not been applied to the assessment of HRV in pediatric OSA. Here, this work aimed to assess HRV using bispectral analysis in children with OSA for signal characterization and diagnostic purposes in two large pediatric databases (0-13 years). The first database (training set) was composed of 981 overnight ECG recordings obtained during polysomnography. The second database (test set) was a subset of the Childhood Adenotonsillectomy Trial database (757 children). We characterized three bispectral regions based on the classic HRV frequency ranges (very low frequency: 0-0.04 Hz; low frequency: 0.04-0.15 Hz; and high frequency: 0.15-0.40 Hz), as well as three OSA-specific frequency ranges obtained in recent studies (BW1: 0.001-0.005 Hz; BW2: 0.028-0.074 Hz; BWRes: a subject-adaptive respiratory region). In each region, up to 14 bispectral features were computed. The fast correlation-based filter was applied to the features obtained from the classic and OSA-specific regions, showing complementary information regarding OSA alterations in HRV. This information was then used to train multi-layer perceptron (MLP) neural networks aimed at automatically detecting pediatric OSA using three clinically defined severity classifiers. Both classic and OSA-specific MLP models showed high and similar accuracy (Acc) and areas under the receiver operating characteristic curve (AUCs) for moderate (classic regions: Acc = 81.0%, AUC = 0.774; OSA-specific regions: Acc = 81.0%, AUC = 0.791) and severe (classic regions: Acc = 91.7%, AUC = 0.847; OSA-specific regions: Acc = 89.3%, AUC = 0.841) OSA levels. Thus, the current findings highlight the usefulness of bispectral analysis on HRV to characterize and diagnose pediatric OSA.

Hierarchical Poincaré analysis for anaesthesia monitoring.

Although the degree of dispersion in Poincaré plots of electroencephalograms (EEG), termed the Poincaré-index, detects the depth of anaesthesia, the Poincaré-index becomes estranged from the bispectral index (BIS) at lighter anaesthesia levels. The present study introduces Poincaré-index20-30 Hz, targeting the 20- to 30-Hz frequency, as the frequency range reported to contain large electromyogram (EMG) portions in frontal EEG. We combined Poincaré-index20-30 Hz with the conventional Poincaré-index0.5-47 Hz using a deep learning technique to adjust to BIS values, and examined whether this layered Poincaré analysis can provide an index of anaesthesia level like BIS. A total of 83,867 datasets of these two Poincaré-indices and BIS-monitor-derived parameters were continuously obtained every 3 s from 30 patients throughout general anaesthesia, and were randomly divided into 75% for a training dataset and 25% for a test dataset. Two Poincaré-indices and two supplemental EEG parameters (EMG70-110 Hz, suppression ratio) in the training dataset were trained in a multi-layer perceptron neural network (MLPNN), with reference to BIS as supervisor. We then evaluated the trained MLPNN model using the test dataset, by comparing the measured BIS (mBIS) with BIS predicted from the model (PredBIS). The relationship between mBIS and PredBIS using the two Poincaré-indices showed a tight linear regression equation: mBIS = 1.00 × PredBIS + 0.15, R = 0.87, p < 0.0001, root mean square error (RMSE) = 7.09, while the relationship between mBIS and PredBIS simply using the original Poincaré-index0.5-47 Hz was weaker (R = 0.82, p < 0.0001, RMSE = 7.32). This suggests the 20- to 30-Hz hierarchical Poincaré analysis has potential to improve on anaesthesia depth monitoring constructed by simple Poincaré analysis.

Prediction of land use/cover change in the Bharathapuzha river basin, India using geospatial techniques.

The Bharathapuzha river basin, once endowed with dense vegetation and abundant water, has been experiencing acute water shortage and extreme climatic conditions in recent times. To understand the influence of human interventions on the natural environmental conditions, including the problems mentioned above, it is essential to critically examine the changes in land use/cover over these years. The objective of this study is to assess land use/cover change in the Bharathapuzha river basin, Kerala during the period 1990-2017 using LANDSAT series satellite images. The dynamics of land use/cover change were quantified and mapped using geospatial techniques. The multi-temporal LANDSAT images were classified by supervised maximum likelihood method to generate the corresponding land use/cover maps; changes in land use/cover in the river basin were subsequently detected by the post-classification technique. Results of the study revealed a drastic change in land use/cover in the period 1990-2017; the primary causes of this were deforestation and urbanization. The near- and long-term future land use/cover maps of the basin for 2020 and 2035 were generated from the historically retrieved land use/cover change pattern. Multi-Layer Perceptron Neural Network and Markov chain techniques were used to generate future land use/cover maps. These maps reveal that the predominant land use/cover class in the basin will be barren land and about 46.13% of the existing (in 2017) dense vegetation will diminish by 2035. The efficiency of sustainable watershed management activities in the river basin can be improved based on the critical observations from this study.

River suspended sediment modelling using the CART model: A comparative study of machine learning techniques.

Suspended sediment load (SSL) modelling is an important issue in integrated environmental and water resources management, as sediment affects water quality and aquatic habitats. Although classification and regression tree (CART) algorithms have been applied successfully to ecological and geomorphological modelling, their applicability to SSL estimation in rivers has not yet been investigated. In this study, we evaluated use of a CART model to estimate SSL based on hydro-meteorological data. We also compared the accuracy of the CART model with that of the four most commonly used models for time series modelling of SSL, i.e. adaptive neuro-fuzzy inference system (ANFIS), multi-layer perceptron (MLP) neural network and two kernels of support vector machines (RBF-SVM and P-SVM). The models were calibrated using river discharge, stage, rainfall and monthly SSL data for the Kareh-Sang River gauging station in the Haraz watershed in northern Iran, where sediment transport is a considerable issue. In addition, different combinations of input data with various time lags were explored to estimate SSL. The best input combination was identified through trial and error, percent bias (PBIAS), Taylor diagrams and violin plots for each model. For evaluating the capability of the models, different statistics such as Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE) and percent bias (PBIAS) were used. The results showed that the CART model performed best in predicting SSL (NSE=0.77, KGE=0.8, PBIAS<±15), followed by RBF-SVM (NSE=0.68, KGE=0.72, PBIAS<±15). Thus the CART model can be a helpful tool in basins where hydro-meteorological data are readily available.

Fusion of ECG and ABP signals based on wavelet transform for cardiac arrhythmias classification.

BACKGROUND AND OBJECTIVE: Each of Electrocardiogram (ECG) and Atrial Blood Pressure (ABP) signals contain information of cardiac status. This information can be used for diagnosis and monitoring of diseases. The majority of previously proposed methods rely only on ECG signal to classify heart rhythms. In this paper, ECG and ABP were used to classify five different types of heart rhythms. To this end, two mentioned signals (ECG and ABP) have been fused. METHODS: These physiological signals have been used from MINIC physioNet database. ECG and ABP signals have been fused together on the basis of the proposed Discrete Wavelet Transformation fusion technique. Then, some frequency features were extracted from the fused signal. To classify the different types of cardiac arrhythmias, these features were given to a multi-layer perceptron neural network. RESULTS: In this study, the best results for the proposed fusion algorithm were obtained. In this case, the accuracy rates of 96.6%, 96.9%, 95.6% and 93.9% were achieved for two, three, four and five classes, respectively. However, the maximum classification rate of 89% was obtained for two classes on the basis of ECG features. CONCLUSIONS: It has been found that the higher accuracy rates were acquired by using the proposed fusion technique. The results confirmed the importance of fusing features from different physiological signals to gain more accurate assessments.

Computer-Assisted Diagnosis System for Breast Cancer in Computed Tomography Laser Mammography (CTLM).

Computed tomography laser mammography (Eid et al. Egyp J Radiol Nucl Med, 37(1): p. 633-643, 1) is a non-invasive imaging modality for breast cancer diagnosis, which is time-consuming and challenging for the radiologist to interpret the images. Some issues have increased the missed diagnosis of radiologists in visual manner assessment in CTLM images, such as technical reasons which are related to imaging quality and human error due to the structural complexity in appearance. The purpose of this study is to develop a computer-aided diagnosis framework to enhance the performance of radiologist in the interpretation of CTLM images. The proposed CAD system contains three main stages including segmentation of volume of interest (VOI), feature extraction and classification. A 3D Fuzzy segmentation technique has been implemented to extract the VOI. The shape and texture of angiogenesis in CTLM images are significant characteristics to differentiate malignancy or benign lesions. The 3D compactness features and 3D Grey Level Co-occurrence matrix (GLCM) have been extracted from VOIs. Multilayer perceptron neural network (MLPNN) pattern recognition has developed for classification of the normal and abnormal lesion in CTLM images. The performance of the proposed CAD system has been measured with different metrics including accuracy, sensitivity, and specificity and area under receiver operative characteristics (AROC), which are 95.2, 92.4, 98.1, and 0.98%, respectively.

Rules versus layers: which side wins the battle of model calibration?

Continuous surface of urbanization suitability, as an input to many urban growth models (UGM), has a significant role on a proper calibration process. The present study evaluates and compares the simulation success of the Cellular Automata-Markov Chain (CA-MC) model through multiple methods. For this, a series of mapping algorithms are applied ranging from empirical methods such as multi-criteria evaluation (MCE) to statistical algorithms without spatially explicit suitability mapping rules such as logistic regression (LR) and multi-layer perceptron (MLP) neural network and finally statistical and spatially explicit rule-based methods such as SLEUTH-Genetic Algorithm (SLEUTH-GA) model. The CA-MC model was calibrated in three study locations including Azadshahr, Gonbad, and Gorgan cities in northeastern Iran. Applying Kappa-based indices (Kappa, K location, K Simulation, and K Transloc) and computing relative error (RE) values of landscape metrics, performance of the model was quantified and compared across the three study sites. The MCE and SLEUTH-GA methods, as the most data-demanding and the most computationally complex methods, respectively, yielded approximately similar results (especially in case of Kappa-based indices) and these methods were less successful compared to LR and MLP models. LR and MLP models were less data-demanding, while they produced approximately equal results. This study concludes that, when historical growth patterns feed an urbanization suitability mapping process, neither rules (SLEUTH-GA) nor layers (MCE) are effectively efficient when applied in a separated manner. Instead, methods with statistical rules and least-correlated input layers (LR and MLP) provide better simulation outputs. In contrast, methods such as MCE are more applicable when a non-path-dependent mapping procedure is desired since this method does not require training data (dependent variable) and the provided flexibilities in urbanization suitability mapping under various scenarios can improve the functionality of land-use change prediction algorithms into innovative land allocation tools.