Support Vector Machine Regression Research Articles

Machine learning-driven web-post buckling resistance prediction for high-strength steel beams with elliptically-based web openings

The use of periodical elliptically-based web (EBW) openings in high strength steel (HSS) beams has been increasingly popular in recent years mainly because of the high strength-to-weight ratio and the reduction in the floor height as a result of allowing different utility services to pass through the web openings. However, these sections are susceptible to web-post buckling (WPB) failure mode and therefore it is imperative that an accurate design tool is made available for prediction of the web-post buckling capacity. Therefore, the present paper aims to implement the power of various machine learning (ML) methods for prediction of the WPB capacity in HSS beams with (EBW) openings and to assess the performance of existing analytical design model. For this purpose, a numerical model is developed and validated with the aim of conducting a total of 10,764 web-post finite element models, considering S460, S690 and S960 steel grades. This data is employed to train and validate different ML algorithms including Artificial Neural Networks (ANN), Support Vector Machine Regression (SVR) and Gene Expression Programming (GEP). Finally, the paper proposes new design models for WPB resistance prediction. The results are discussed in detail, and they are compared with the numerical models and the existing analytical design method. The proposed design models based on the machine learning predictions are shown to be powerful, reliable and efficient design tools for capacity predictions of the WPB resistance of HSS beams with periodical (EBW) openings.

Construction and evaluation of endometriosis diagnostic prediction model and immune infiltration based on efferocytosis-related genes.

Background: Endometriosis (EM) is a long-lasting inflammatory disease that is difficult to treat and prevent. Existing research indicates the significance of immune infiltration in the progression of EM. Efferocytosis has an important immunomodulatory function. However, research on the identification and clinical significance of efferocytosis-related genes (EFRGs) in EM is sparse. Methods: The EFRDEGs (differentially expressed efferocytosis-related genes) linked to datasets associated with endometriosis were thoroughly examined utilizing the Gene Expression Omnibus (GEO) and GeneCards databases. The construction of the protein-protein interaction (PPI) and transcription factor (TF) regulatory network of EFRDEGs ensued. Subsequently, machine learning techniques including Univariate logistic regression, LASSO, and SVM classification were applied to filter and pinpoint diagnostic biomarkers. To establish and assess the diagnostic model, ROC analysis, multivariate regression analysis, nomogram, and calibration curve were employed. The CIBERSORT algorithm and single-cell RNA sequencing (scRNA-seq) were employed to explore immune cell infiltration, while the Comparative Toxicogenomics Database (CTD) was utilized for the identification of potential therapeutic drugs for endometriosis. Finally, immunohistochemistry (IHC) and reverse transcription quantitative polymerase chain reaction (RT-qPCR) were utilized to quantify the expression levels of biomarkers in clinical samples of endometriosis. Results: Our findings revealed 13 EFRDEGs associated with EM, and the LASSO and SVM regression model identified six hub genes (ARG2, GAS6, C3, PROS1, CLU, and FGL2). Among these, ARG2, GAS6, and C3 were confirmed as diagnostic biomarkers through multivariate logistic regression analysis. The ROC curve analysis of GSE37837 (AUC = 0.627) and GSE6374 (AUC = 0.635), along with calibration and DCA curve assessments, demonstrated that the nomogram built on these three biomarkers exhibited a commendable predictive capacity for the disease. Notably, the ratio of nine immune cell types exhibited significant differences between eutopic and ectopic endometrial samples, with scRNA-seq highlighting M0 Macrophages, Fibroblasts, and CD8 Tex cells as the cell populations undergoing the most substantial changes in the three biomarkers. Additionally, our study predicted seven potential medications for EM. Finally, the expression levels of the three biomarkers in clinical samples were validated through RT-qPCR and IHC, consistently aligning with the results obtained from the public database. Conclusion: we identified three biomarkers and constructed a diagnostic model for EM in this study, these findings provide valuable insights for subsequent mechanistic research and clinical applications in the field of endometriosis.

EVALUATION OF SPECTRAL INDICES FOR DETECTION OF BURNED AREAS IN AN ENVIRONMENTAL PROTECTION AREA USING SPOT-5 IMAGES

The Cerrado biome has great importance in ecological biodiversity, but deforestation has accelerated in recent decades. The Rio Preto-BA Environmental Preservation Area, known for the high agricultural activity of grains, continually suffers from forest fires and loss of native vegetation. Satellite remote sensing is proposed as an alternative to accurately locate and quantify fire-affected surfaces and their impacts on the landscape. With the recent free availability of SPOT-5 images, it has made it possible to detect burned areas with considerable spatial and spectral resolution. The objective of the present study was to determine the ideal spectral index for detection of the burned area inserted in the APA Rio Preto through two SPOT-5 scenes and the application of the Support Vector Machines (SVM) model. The burned areas detected were submitted to the calculation of five spectral indices, BAI, BAIM, NBR, NDVI and EVI2. The ability of each index to discriminate burned areas was estimated by comparing them with each other using a statistical separability index, SVM regression and accuracy analysis. BAIM was identified as the index with the greatest potential for discriminating burnt areas with maximum separability and classification accuracy above 90%, while the NDVI and EVI2 indices had low performance. It is hoped that these results can be used to evaluate and prioritize monitoring areas, contribute to the implementation of a fire management plan in the APA and to support subsequent studies on fire dynamics in forest systems integrated with advanced computational technologies.

Fault location of distribution network with distributed generation based on Karrenbauer transform and support vector machine regression

Fault location of distribution network with distributed generation based on Karrenbauer transform and support vector machine regression

Rapid quantitative authentication and analysis of camellia oil adulterated with edible oils by electronic nose and FTIR spectroscopy

Camellia oil, recognized as a high-quality edible oil endorsed by the Food and Agriculture Organization, is confronted with authenticity issues arising from fraudulent adulteration practices. These practices not only pose health risks but also lead to economic losses. This study proposes a novel machine learning framework, referred to as a transformer encoder backbone with a support vector machine regressor (TES), coupled with an electronic nose (E-nose), for detecting varying adulteration levels in camellia oil. Experimental results indicate that the proposed TES model exhibits the best performance in identifying the adulterated concentration of camellia oi, compared with five other machine learning models (the support vector machine, random forest, XGBoost, K-nearest neighbors, and backpropagation neural network). The results obtained by E-nose detection are verified by complementary Fourier transform infrared (FTIR) spectroscopy analysis for identifying functional groups, ensuring accuracy and providing a comprehensive assessment of the types of adulterants. The proposed TES model combined with E-nose offers a rapid, effective, and practical tool for detecting camellia oil adulteration. This technique not only safeguards consumer health and economic interests but also promotes the application of E-nose in market supervision.

Accurate estimation of prefabricated building construction cost based on support vector machine regression

Accurate estimation of prefabricated building construction cost based on support vector machine regression

Reducing soil and leaf shadow interference in UAV imagery for cotton nitrogen monitoring.

Individual leaves in the image are partly veiled by other leaves, which create shadows on another leaf. To eliminate the interference of soil and leaf shadows on cotton spectra and create reliable monitoring of cotton nitrogen content, one classification method to unmanned aerial vehicle (UAV) image pixels is proposed. In this work, green light (550 nm) is divided into 10 levels to limit soil and leaf shadows (LS) on cotton spectrum. How many shadow has an influence on cotton spectra may be determined by the strong correlation between the vegetation index (VI) and leaf nitrogen content (LNC). Several machine learning methods were utilized to predict LNC using less disturbed VI. R-Square (R 2), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate the performance of the model. (i) after the spectrum were preprocessed by gaussian filter (GF), SG smooth (SG), and combination of GF and SG (GF&SG), the significant relationship between VI and LNC was greatly improved, so the Standard deviation of datasets was also decreased greatly; (ii) the image pixels were classified twice sequentially. Following the first classification, the influence of soil on vegetation index (VI) decreased. Following secondary classification, the influence of soil and LS to VI can be minimized. The relationship between the VI and LNC had improved significantly; (iii) After classifying the image pixels, the VI of 2-3, 2-4, and 2-5 have a stronger relationship with LNC accordingly. Correlation coefficients (r) can reach to 0.5. That optimizes monitoring performance when combined with GF&SG to predict LNC, support vector machine regression (SVMR) has the better performance, R 2, RMSE, and MAE up to 0.86, 1.01, and 0.71, respectively. The UAV image classification technique in this study can minimize the negative effects of soil and LS on cotton spectrum, allowing for efficient and timely predict LNC.

Accurate estimation of prefabricated building construction cost based on support vector machine regression

Accurate estimation of prefabricated building construction cost based on support vector machine regression

Exploring the machine learning approach to the cultural and ecological sustainable development of Fuzhou tea picking opera

Abstract The article introduces machine learning based on a support vector machine to illustrate the scenario of Fuzhou Tea Casting Opera, a folkloric non-heritage research and ecologically sustainable development, from the perspective of economic return prediction. Fine-grained information recognition and extraction of massive, unstructured UGC data are used for data collection preprocessing in this paper. Mostly, the relationship is established by analyzing the training samples to establish the connection between input and output data. The limited sample information is used to find the best compromise between the model’s complexity and learning ability. Then, search for the parameters that make the geometric interval the largest so as to construct the function and obtain the optimal hyperplane. In this paper, the kernel function is introduced using a ɛ -insensitive loss function. To obtain the decision function in different cases, two cases are modeled using support vector machine regression. Finally, the support vector machine model is used to predict the economic benefits of the Fuzhou tea-picking operation. The SVM prediction model’s prediction error of the economic benefits of tea-picking opera for the nine years from 2010 to 2018 is always maintained at a safe value below 0.035. The accuracy and stability of the prediction effect of the decision tree model and the gray prediction model are not as good as this paper’s model. It shows that the model presented in this paper is more suitable for predicting the benefits of the cultural and ecological sustainable development of Fuzhou tea-picking opera.

Inversion model of soil salinity in alfalfa covered farmland based on sensitive variable selection and machine learning algorithms.

Timely and accurate monitoring of soil salinity content (SSC) is essential for precise irrigation management of large-scale farmland. Uncrewed aerial vehicle (UAV) low-altitude remote sensing with high spatial and temporal resolution provides a scientific and effective technical means for SSC monitoring. Many existing soil salinity inversion models have only been tested by a single variable selection method or machine learning algorithm, and the influence of variable selection method combined with machine learning algorithm on the accuracy of soil salinity inversion remain further studied. Firstly, based on UAV multispectral remote sensing data, by extracting the spectral reflectance of each sampling point to construct 30 spectral indexes, and using the pearson correlation coefficient (PCC), gray relational analysis (GRA), variable projection importance (VIP), and support vector machine-recursive feature elimination (SVM-RFE) to screen spectral index and realize the selection of sensitive variables. Subsequently, screened and unscreened variables as model input independent variables, constructed 20 soil salinity inversion models based on the support vector machine regression (SVM), back propagation neural network (BPNN), extreme learning machine (ELM), and random forest (RF) machine learning algorithms, the aim is to explore the feasibility of different variable selection methods combined with machine learning algorithms in SSC inversion of crop-covered farmland. To evaluate the performance of the soil salinity inversion model, the determination coefficient (R2), root mean square error (RMSE) and performance deviation ratio (RPD) were used to evaluate the model performance, and determined the best variable selection method and soil salinity inversion model by taking alfalfa covered farmland in arid oasis irrigation areas of China as the research object. The variable selection combined with machine learning algorithm can significantly improve the accuracy of remote sensing inversion of soil salinity. The performance of the models has been improved markedly using the four variable selection methods, and the applicability varied among the four methods, the GRA variable selection method is suitable for SVM, BPNN, and ELM modeling, while the PCC method is suitable for RF modeling. The GRA-SVM is the best soil salinity inversion model in alfalfa cover farmland, with Rv 2 of 0.8888, RMSEv of 0.1780, and RPD of 1.8115 based on the model verification dataset, and the spatial distribution map of soil salinity can truly reflect the degree of soil salinization in the study area. Based on our findings, the variable selection combined with machine learning algorithm is an effective method to improve the accuracy of soil salinity remote sensing inversion, which provides a new approach for timely and accurate acquisition of crops covered farmland soil salinity information.

Performance Evaluation of Support Vector Regression Machine Models in Water Level Forecasting

Understanding and predicting water level rise is crucial for effective water resource management, flood control, disaster risk reduction, and adaptation to climate change. This study utilize the use of Support Vector Regression Machine (SVRM) models in predicting the water level of Mandulog River in the Philippines. To evaluate its predictive capability, two data architectures, various kernels, and parameters were examined to find the optimal configuration. Multiple kernels were compared for accurate pattern discovery, and parameter selection was performed to enhance predictive accuracy. The SVRM models achieved the lowest Mean Average Percentage Error (MAPE) of 2.186%, Root-Mean Squared Error (RMSE) of 0.00601 during the dry season, and MAPE of 2.226% and RMSE of 0.00602 during the rainy season. Both models were deemed suitable for watershed level forecasting.

A Potential-Real-Time Thigh Orientation Prediction Method Based on Two Shanks-Mounted IMUs and Its Clinical Application

The detection and evaluation of gait kinematics is vital for patient diagnosis and rehabilitation. Aiming at limitations of commonly used optical capture and wearable sensing systems in clinical applications, this paper proposes a thigh attitude angle prediction method based on the hip error tolerance from the kinematic data of two shank-mounted IMUs (Inertial Measurement Unit). The novelties of the proposed method are summarized as follows: i) It develops a parallel approach to regress variation of hip error tolerance for different subjects. This parallel approach, by simultaneously deconstructing the shank kinematics data via different regression algorithm including support vector machine, boosting tree, and stepwise linearity regression, is able to well accommodate the characteristics of both health subjects and patients. ii) It develops some evaluation indices based on gait symmetry, consistency, and activity to fully evaluate the human lower limbs motion performance in gait by only two IMUs. The effectiveness of the proposed method is verified by the experimental results among 8 healthy subjects and 16 cerebral infarction patients. For the healthy subjects, the estimated error of thigh prediction compared with Xsens and Vicon are <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.3 \pm {0.3^ \circ }$</tex-math> </inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.5 \pm {0.7^ \circ }$</tex-math> </inline-formula> , respectively. For the patients, the estimated error compared with Xsens-measured angle is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.8 \pm {1.7^ \circ }$</tex-math> </inline-formula> . Its broad significance in actual intelligent healthcare and robotics-assisted rehabilitation is three-fold: First, it is a recursive real-time method as it only needs data from the previous gait cycle to predict the thigh angle. Second, its accuracy meets the actual clinical needs. Third, it is a low-cost method that only needs two IMUs and has high potentials of clinical applications. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Gait is of great significance to quantify the degree of movement disorders in clinical practice, and the existing equipment is rarely able to meet the needs of full dimension, low cost, and low place constraints in clinical practice. This paper presents an innovative gait kinematic prediction method, which only uses two IMUs attached to the shanks to predict the orientation of thigh. Compared with related works, the proposed methods have achieved high-precision, real-time and low-cost acquisition. This paper is inspired by the problems of large number, high motion interference, and high cost of wearable gait measurement devices in clinical practice. The proposed method have potential to be integrated into exoskeletons and medical walking AIDS, which could greatly improve the comfort and control precision of the wearable system.

Using Machine Learning to Diagnose Depression: A Review of Research Trends and Algorithms

Depression is one of the most common mental health illnesses that affects millions of people worldwide. In the recent past, there have been numerous studies on depression and mental health at large. Artificial Intelligence (AI) technologies have increasingly been adopted to solve complex human-related problems including the diagnosis and treatment of different diseases. Researchers have been trying to establish if and how Machine Learning (ML), a branch of AI, can be used to diagnose depression to help solve this complex problem of mental health. In this paper, we seek to review and establish unique research trends in using ML algorithms to diagnose depression as well as establish the most recommended ML algorithm that has shown high accuracy and hence mostly been recommended by many research articles. The study consisted of 2 phases where in the first phase, 3 major publishers and journals were considered to establish research trends. The phase considered articles published between 2015 to 2023. In the second phase, a total of 20 journal articles with open access and having been published between 2020 and 2023 were considered to establish the most recommended ML algorithm for solving the problem of diagnosing depression among people. Results from phase 1 of the analysis indicated a sharp increase in research trends in this area. In phase 2, different research articles showed varied accuracy with Deep Learning (DL) showing a high level of accuracy. 4 out of the 7 studies that recommended the use of DL as the preferred algorithm showed an accuracy level of over 96%. The same trends were observed in related statistical measures i.e., F1-Score and Area Under the ROC Curve (AUC) where DL outperformed the other ML algorithms. In some studies, it achieved a high score of 0.995 in AUC and 0.98 in F1-Score. DL was mostly used when a large amount of data was considered i.e., survey and social media data. However, other algorithms like Support Vector Machine (SVM), XGBoost, and Logistic Regression also performed well. Generally, ML and DL proved to be an efficient tool that can be used to diagnose depression by using data from different sources i.e., social media, Electronic Medical Records (EMR), surveys, interviews, and wearables among others. Though DL proved to be an efficient tool to predict depression, in other studies, other ML algorithms had a relatively good performance and were recommended. This raises the need to compare all the algorithms on a case-by-case basis before deciding which one to use.

Study on prediction of early adverse events by CapeOX therapy in patients with colorectal cancer.

CapeOX is a regimen used as postoperative adjuvant chemotherapy for the treatment of advanced recurrent colorectal cancer. If early adverse events occur, treatment may not progress as planned and further dose reduction may be necessary. In this study, we investigated whether pre-treatment medical records could be used to predict adverse events in order to prevent adverse events caused by CapeOX treatment. The 178 patients were classified into two groups (97 in the adverse event positive group and 81 in the adverse event-negative group) based on withdrawal or postponement of four or fewer courses. In univariate analysis, age, height, weight, body surface area (BSA), creatinine clearance, muscle mass, and lean body mass were associated with early adverse events (P<0.05). The area under the receiver operating characteristic curve obtained by Stepwise logistic regression analysis using the Akaike information criterion method was 0.832. For nested k-fold cross validation, the accuracy rates of the support vector machine, random forest, and logistic regression algorithms were 0.71, 0.70, and 0.75, respectively. The results of the present study suggest that a logistic regression prediction model may be useful in predicting early adverse events caused by CapeOX therapy in patients with colorectal cancer. J. Med. Invest. 71 : 141-147, February, 2024.

Genetic biomarker prediction based on gender disparity in asthma throughout machine learning.

Asthma is a chronic respiratory condition affecting populations worldwide, with prevalence ranging from 1-18% across different nations. Gender differences in asthma prevalence have attracted much attention. The aim of this study was to investigate biomarkers of gender differences in asthma prevalence based on machine learning. The data came from the gene expression omnibus database (GSE69683, GSE76262, and GSE41863), which involved in a number of 575 individuals, including 240 males and 335 females. Theses samples were divided into male group and female group, respectively. Grid search and cross-validation were employed to adjust model parameters for support vector machine, random forest, decision tree and logistic regression model. Accuracy, precision, recall, and F1 score were used to evaluate the performance of the models during the training process. After model optimization, four machine learning models were utilized to predict biomarkers of sex differences in asthma. In order to validate the accuracy of our results, we performed Wilcoxon tests on the genes expression. In datasets GSE76262 and GSE69683, support vector machine, random forest, logistic regression, and decision tree all achieve 100% accuracy, precision, recall, and F1 score. Our findings reveal that XIST serves as a common biomarker among the three samples, comprising a total of 575 individuals, with higher expression levels in females compared to males (p < 0.01). XIST serves as a genetic biomarker for gender differences in the prevalence of asthma.

Machine Learning for Test Case Prioritization in Continuous Integration: A Comprehensive Analysis

Machine Learning for Test Case Prioritization in Continuous Integration: A Comprehensive Analysis

Decoding emotional resilience in aging: unveiling the interplay between daily functioning and emotional health.

EPs pose significant challenges to individual health and quality of life, attracting attention in public health as a risk factor for diminished quality of life and healthy life expectancy in middle-aged and older adult populations. Therefore, in the context of global aging, meticulous exploration of the factors behind emotional issues becomes paramount. Whether ADL can serve as a potential marker for EPs remains unclear. This study aims to provide new evidence for ADL as an early predictor of EPs through statistical analysis and validation using machine learning algorithms. Data from the 2018 China Health and Retirement Longitudinal Study (CHARLS) national baseline survey, comprising 9,766 samples aged 45 and above, were utilized. ADL was assessed using the BI, while the presence of EPs was evaluated based on the record of "Diagnosed with Emotional Problems by a Doctor" in CHARLS data. Statistical analyses including independent samples t-test, chi-square test, Pearson correlation analysis, and multiple linear regression were conducted using SPSS 25.0. Machine learning algorithms, including Support Vector Machine (SVM), Decision Tree (DT), and Logistic Regression (LR), were implemented using Python 3.10.2. Population demographic analysis revealed a significantly lower average BI score of 65.044 in the "Diagnosed with Emotional Problems by a Doctor" group compared to 85.128 in the "Not diagnosed with Emotional Problems by a Doctor" group. Pearson correlation analysis indicated a significant negative correlation between ADL and EPs (r = -0.165, p < 0.001). Iterative analysis using stratified multiple linear regression across three different models demonstrated the persistent statistical significance of the negative correlation between ADL and EPs (B = -0.002, β = -0.186, t = -16.476, 95% CI = -0.002, -0.001, p = 0.000), confirming its stability. Machine learning algorithms validated our findings from statistical analysis, confirming the predictive accuracy of ADL for EPs. The area under the curve (AUC) for the three models were SVM-AUC = 0.700, DT-AUC = 0.742, and LR-AUC = 0.711. In experiments using other covariates and other covariates + BI, the overall prediction level of machine learning algorithms improved after adding BI, emphasizing the positive effect of ADL on EPs prediction. This study, employing various statistical methods, identified a negative correlation between ADL and EPs, with machine learning algorithms confirming this finding. Impaired ADL increases susceptibility to EPs.

Hybrid grey wolf and whale optimization for enhanced Parkinson's prediction based on machine learning models using biomedical sound

BackgroundBiomedical voice measurements have been used by many physicians and scientists to distinguish Parkinson's patients from ordinary people. Measurements of biomedical voices involve many variables calculated from signal analysis of the voice. These variables can be used to distinguish Parkinson's patients from non-Parkinson's patients. Unfortunately, using all computed variables may be ineffective and inaccurate due to the complexity of establishing a relationship between all the input variables and Parkinson's states. MethodsThis paper describes the development of a hybrid optimizer by combining two optimization algorithms: the Grey Wolf Optimizer and the Whale Optimizer. The hybrid optimizer enhances feature selection to provide a fast and robust Parkinson's prediction model. Additionally, this research incorporates five other feature selection algorithms for comparison purposes: Ranker, Greedy, BestFirst, Hybrid Grey Wolf Optimization, and Whale Optimization. The outputs from these algorithms are fed into six prediction models to determine the most accurate combination. These models include the neural network, Quest, Chi-squared Automatic Interaction Detection, support vector machine, CR-tree, and logistic regression models. Subsequently, the developed models are compared to identify the most accurate model based on various performance metrics. ResultsThe combination of the hybrid grey wolf and whale models yielded the highest scores in most metrics, achieving a perfect recall and a high F1 score. All models generated similar output, with an accuracy greater than 0.89. Quest, CR-tree, and neural networks are the most reliable and accurate models. ConclusionsBiomedical sound measurements can be used to develop an accurate and cost-effective Parkinson's prediction model.

IoT enabled Intelligent Energy Management System employing advanced forecasting algorithms and load optimization strategies to enhance renewable energy generation

Effectively utilizing renewable energy sources while avoiding power consumption restrictions is the problem of demand-side energy management. The goal is to develop an intelligent system that can precisely estimate energy availability and plan ahead for the next day in order to overcome this obstacle. The Intelligent Smart Energy Management System (ISEMS) described in this work is designed to control energy usage in a smart grid environment where a significant quantity of renewable energy is being introduced. The proposed system evaluates various predictive models to achieve accurate energy forecasting with hourly and day-ahead planning. When compared to other predictive models, the Support Vector Machine (SVM) regression model based on Particle Swarm Optimization (PSO) seems to have better performance accuracy. Then, using the anticipated requirements, the experimental setup for ISEMS is shown, and its performance is evaluated in various configurations while considering features that are prioritized and associated with user comfort. Furthermore, Internet of Things (IoT) integration is put into practice for monitoring at the user end.

Maternal Health Risk Prediction Using Machine Learning Methods

This work employed machine learning methods – decision tree algorithm, support vector machine (SVM) and logistic regression to predict health risk outcome of pregnant women based on their vital signs. The aim was to determine which of the model most suitable for the prediction of health risk for any pregnant woman. Evaluation of the methods were done using Accuracy, Precision, F1-Score and recall. The results revealed that the Decision tree model achieved the highest accuracy of 0.8669 (86.99%), that indicates correct prediction in 86.99% of the cases. Also, it achieved the highest precision for all the risk categories (high_risk, low_risk and mid_risk) with values (87%, 86% and 75% respectively) implying lower likelihood of false positive predictions for all risk categories. The Decision Tree model appears to be a promising approach for predicting the impact of vital signs on health risk of pregnant mothers. It exhibited high precision, high recall (sensitivity) and a balanced F1-score, suggesting accurate predictions with very low rate of false positives.