Predicting Diabetes Using Machine Learning Techniques

The area of corporate bankruptcy prediction attains high economic importance, as it affects many stakeholders. The prediction of corporate bankruptcy has been extensively studied in economics, accounting and decision sciences over the past two decades. The corporate bankruptcy prediction has been a matter of talk among academic literature and professional researchers throughout the world. Different traditional approaches were suggested based on hypothesis testing and statistical modeling. Therefore, the primary purpose of the research is to come up with a model that can estimate the probability of corporate bankruptcy by evaluating its occurrence of failure using different machine learning models. As the dataset was not well prepared and contains missing values, various data mining and data pre-processing techniques were utilized for data preparation. Within this research, the task of resolving the issues induced by the imbalance between the two classes is approached by applying different data balancing techniques. We address the problem of imbalanced data with the random undersampling and Synthetic Minority Over Sampling Technique (SMOTE). We used five machine learning models (support vector machine, J48 decision tree, Logistic model tree, random forest and decision forest) to predict corporate bankruptcy earlier to the occurrence. We use data from 2009 to 2013 on Poland manufacturing corporates and selected the 64 financial indicators to be broken down. The main finding of the study is a significant improvement in predictive accuracy using machine learning techniques. We also include other economic indicators ratios, along with Altman’s Z-score variables related to profitability, liquidity, leverage and solvency (short/long term) to propose an efficient model. Machine learning models give better results while balancing the data through SMOTE as compared to random undersampling. The machine learning technique related to decision forest led to 99% accuracy, whereas support vector machine (SVM), J48 decision tree, Logistic Model Tree (LMT) and Random Forest (RF) led to 92%, 92.3%, 93.8% and 98.7% accuracy, respectively, with all predictive financial indicators. We find that the decision forest outperforms the other techniques and previous techniques discussed in the literature. The proposed method is also deployed on the web to assist regulators, investors, creditors and scholars to predict corporate bankruptcy.

Diabetes is a major chronic syndrome caused by a series of metabolic abnormalities in which blood glucose levels are abnormally high for an indeterminate amount of time. It influences various organs in the human body, resulting in a variety of complex diseases such as stroke, renal disease, pulmonary embolism, eyesight, and so on. Diabetes Disorders (DD) are presently one of the healthcare top causes of mortality. Predictive analytics in the health care system is a huge obstacle, but if accurate early prediction is achieved, the potential risk and degree of diabetes may be significantly decreased. Machine learning (ML) techniques are now used to analyze medical datasets at an earlier stage of life in keeping people safe. In this research, we utilized several ML approaches notably Logistic Regression, Decision Tree (DT), XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) on PIMA Indian Diabetes Dataset in order to monitor and evaluate their performances in diabetes prediction. The performance of the various ML algorithms employed in this research suggests which algorithm is most suitable in diabetes prediction. It is observed that among all the models XGBoost had outperformed the other ML techniques with an accuracy of 80.73% while SVM was the second-best performing model with a classification accuracy of 80.21%. Thus, employing ML techniques, this study aims to assist doctors as well as clinicians in the early detection of diabetes.

Detection of visual faults in photovoltaic modules using a stacking ensemble approach

Emotion recognition plays a pivotal role in advancing Human-Computer Interaction (HCI) by enabling systems to understand and respond to human emotional states. Among various modalities, Speech Emotion Recognition (SER) stands out as a non-invasive, cost-effective, and temporally efficient approach for detecting emotions. This study leverages the RAVDESS dataset, which provides high-quality audio recordings for emotion classification. The proposed methodology involves preprocessing audio signals to remove noise, extracting temporal and spectral features in both time and frequency domains, and implementing machine learning models for multi-class emotion classification. The study evaluates and compares the performance of several classification models, including Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and Decision Trees (DT). Experimental results demonstrate promising accuracy levels, highlighting the potential of these machine learning techniques in SER. This research contributes to the integration of SER within Brain-Computer Interfaces (BCI) and other emotion-aware applications, paving the way for enhanced interactive systems. Keywords – Speech Emotion Recognition -Machine Learning - Random Forest -Multilayer Perceptron - Support Vector Machine - Convolutional Neural Networks - Decision Tree - RAVDESS Dataset

Landslide disaster risk reduction necessitates the investigation of different geotechnical causal factors for slope failures. Machine learning (ML) techniques have been proposed to study causal factors across many application areas. However, the development of ensemble ML techniques for identifying the geotechnical causal factors for slope failures and their subsequent prediction has lacked in literature. The primary goal of this research is to develop and evaluate novel feature selection methods for identifying causal factors for slope failures and assess the potential of ensemble and individual ML techniques for slope failure prediction. Twenty-one geotechnical causal factors were obtained from 60 sites (both landslide and non-landslide) spread across a landslide-prone area in Mandi, India. Relevant causal factors were evaluated by developing a novel ensemble feature selection method that involved an average of different individual feature selection methods like correlation, information-gain, gain-ratio, OneR, and F-ratio. Furthermore, different ensemble ML techniques (Random Forest (RF), AdaBoost (AB), Bagging, Stacking, and Voting) and individual ML techniques (Bayesian network (BN), decision tree (DT), multilayer perceptron (MLP), and support vector machine (SVM)) were calibrated to 70% of the locations and tested on 30% of the sites. The ensemble feature selection method yielded six major contributing parameters to slope failures: relative compaction, porosity, saturated permeability, slope angle, angle of the internal friction, and in-situ moisture content. Furthermore, the ensemble RF and AB techniques performed the best compared to other ensemble and individual ML techniques on test data. The present study discusses the implications of different causal factors for slope failure prediction.

With the rapid developments in medical technologies and computers, it is getting easier to detect neurological disorders. However, the early detection of Epilepsy which is a common and important neurological disorder is still a crucial and challenging procedure. Like the other disorders, early detection gives some chances for controlling and smoothing of the effects of the disease. Developing early detection methods for epileptic seizures from Electroencephalography (EEG) signals using signal processing and machine learning techniques has been a popular research area in last decades. In this work, we propose to use some time-domain features together with same features extracted for sub-signals of the original signal. For obtaining sub-signals from EEG signal, we used classical discrete-time wavelet decomposition. All EEG signals have been decomposed to five important sub-bands. After extracting features from EEG signals and all sub-signals, we have applied the feature matrices to some machine learning algorithms. Most of the researches in this area distinguish only two or three states of epileptic seizures. In contrary to majority, we preferred to classify all five classes from EEG signals. Classification results of our method show an important success comparing with the other researches. Using statistical, auto-correlation based and wavelet transform based features of EEG signals together to predict and detect epilepsy makes our method robust. In this work, we present the comparison of machine learning techniques for the detection of epilepsy using electroencephalography (EEG) signals. The five states of epilepsy are classified using Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Multilayer Perceptrons (MLP), k-Nearest Neighbors (k-NN), and Support Vector Machines (SVM) algorithms. Results show that Random Forest, Multilayer Perceptrons, k-Nearest Neighbors (k-NN) with 10-fold cross-validation are the most successful algorithms with 92% accuracy.

Diabetes mellitus is a group of metabolic diseases in which blood sugar levels are too high. About 8.8% of the world was diabetic in 2017. It is projected that this will reach nearly 10% by 2045. The major challenge is that when machine learning-based classifiers are applied to such data sets for risk stratification, leads to lower performance. Thus, our objective is to develop an optimized and robust machine learning (ML) system under the assumption that missing values or outliers if replaced by a median configuration will yield higher risk stratification accuracy. This ML-based risk stratification is designed, optimized and evaluated, where: (i) the features are extracted and optimized from the six feature selection techniques (random forest, logistic regression, mutual information, principal component analysis, analysis of variance, and Fisher discriminant ratio) and combined with ten different types of classifiers (linear discriminant analysis, quadratic discriminant analysis, naïve Bayes, Gaussian process classification, support vector machine, artificial neural network, Adaboost, logistic regression, decision tree, and random forest) under the hypothesis that both missing values and outliers when replaced by computed medians will improve the risk stratification accuracy. Pima Indian diabetic dataset (768 patients: 268 diabetic and 500 controls) was used. Our results demonstrate that on replacing the missing values and outliers by group median and median values, respectively and further using the combination of random forest feature selection and random forest classification technique yields an accuracy, sensitivity, specificity, positive predictive value, negative predictive value and area under the curve as: 92.26%, 95.96%, 79.72%, 91.14%, 91.20%, and 0.93, respectively. This is an improvement of 10% over previously developed techniques published in literature. The system was validated for its stability and reliability. RF-based model showed the best performance when outliers are replaced by median values.

This study aims to assess the performance of machine learning (ML) techniques in optimising nurse staffing and evaluating the appropriateness of nursing care delivery models in hospital wards. The primary outcome measures include the adequacy of nurse staffing and the appropriateness of the nursing care delivery system. Historical and current healthcare challenges, such as nurse shortages and increasing patient acuity, necessitate innovative approaches to nursing care delivery. For instance, the COVID-19 pandemic highlighted the need for flexible and scalable staffing models to manage surges in patient volume and acuity. A descriptive study was conducted in 39 inpatient wards across a university hospital and three state hospitals, involving 117 ward-level observations. Data were collected using the Rush Medicus Patient Classification Scale and analysed using k-Nearest Neighbour, Support Vector Machine, Random Forest, and Logistic Regression algorithms. Effectiveness was measured by the accuracy of machine learning predictions regarding nurse staffing adequacy, while suitability was determined by the congruence between observed nursing care models and patient needs. STROBE checklist. The Random Forest algorithm demonstrated the highest accuracy in predicting both nurse staffing adequacy and the appropriateness of nursing care delivery systems. The study found that 68.4% of wards had sufficient nurse staffing and 26.5% of wards used appropriate care delivery models, with functional nursing and total patient care models being the most commonly used. The study highlights functional nursing and total patient care models, emphasising the need to consider nurse qualifications and patient needs in selecting care systems. Machine learning, particularly the Random Forest algorithm, proved effective in aligning staffing with patient requirements. Machine learning, particularly the Random Forest algorithm, proves effective in optimising nursing care delivery models, suggesting significant potential for enhancing patient care and nurse satisfaction. The research underscores machine learning's role in improving nursing care delivery, aligning nurse staffing with patient needs, and advancing healthcare outcomes. The findings advocate for integrating machine learning in the planning of nursing care delivery models. This study sets a precedent for using data-driven approaches to improve nurse staffing and care delivery, potentially enhancing global clinical outcomes and operational efficiencies. The global clinical community can learn from this study the value of employing machine learning techniques to make informed, evidence-based decisions in healthcare management. While the study lacked direct patient involvement, its goal was to enhance patient care and healthcare efficiency. Future research will aim to incorporate patient and public insights more directly.

Learning disabilities (LD) affect a significant number of school-age children and often go undiagnosed due to the limitations of traditional evaluation methods, which are time-consuming, resource-intensive, and reliant on subjective interpretation. The early and accurate identification of LD is essential for implementing timely educational interventions that can improve the academic and social development of affected students. This research aims to develop an automated system that leverages machine learning (ML) techniques to predict learning disabilities based on a variety of input features derived from behavioral, cognitive, academic, and psychological assessments. A dataset comprising relevant indicators—such as reading and writing abilities, attention span, memory retention, logical reasoning, language skills, and classroom behavior—was collected and preprocessed for analysis. Feature selection techniques were employed to identify the most predictive attributes. Several machine learning models were implemented and compared, including Decision Trees, Support Vector Machines (SVM), Random Forest, k- Nearest Neighbors (k-NN), and Logistic Regression. Each model was trained and validated using standard cross-validation techniques to ensure generalization and robustness. Among the evaluated algorithms, the Random Forest classifier yielded the highest accuracy, outperforming others in terms of precision, recall, F1-score, and overall predictive performance. The results demonstrate that machine learning models, when trained on appropriate features, can effectively predict the likelihood of a learning disability in children with high accuracy and reliability. The proposed system not only aids educators, parents, and healthcare professionals in early detection but also reduces dependence on extensive manual testing. By integrating such intelligent diagnostic tools into educational institutions, it becomes possible to facilitate early intervention strategies, personalize learning plans, and ultimately enhance the learning experience and developmental trajectory of students at risk.

This paper presents a study based on data analysis of the sarcopenia level in older adults. Sarcopenia is a prevalent pathology in adults of around 50 years of age, whereby the muscle mass decreases by 1 to 2% a year, and muscle strength experiences an annual decrease of 1.5% between 50 and 60 years of age, subsequently increasing by 3% each year. The World Health Organisation estimates that 5–13% of individuals of between 60 and 70 years of age and 11–50% of persons of 80 years of age or over have sarcopenia. This study was conducted with 166 patients and 99 variables. Demographic data was compiled including age, gender, place of residence, schooling, marital status, level of education, income, profession, and financial support from the State of Baja California, and biochemical parameters such as glycemia, cholesterolemia, and triglyceridemia were determined. A total of 166 patients took part in the study, with an average age of 77.24 years. The purpose of the study was to provide an automatic classifier of sarcopenia level in older adults using artificial intelligence in addition to identifying the weight of each variable used in the study. We used machine learning techniques in this work, in which 10 classifiers were employed to assess the variables and determine which would provide the best results, namely, Nearest Neighbors (3), Linear SVM (Support Vector Machines) (C = 0.025), RBF (Radial Basis Function) SVM (gamma = 2, C = 1), Gaussian Process (RBF (1.0)), Decision Tree (max_depth = 3), Random Forest (max_depth=3, n_estimators = 10), MPL (Multilayer Perceptron) (alpha = 1), AdaBoost, Gaussian Naive Bayes, and QDA (Quadratic Discriminant Analysis). Feature selection determined by the mean for the variable ranking suggests that Age, Systolic Arterial Hypertension (HAS), Mini Nutritional Assessment (MNA), Number of chronic diseases (ECNumber), and Sodium are the five most important variables in determining the sarcopenia level, and are thus of great importance prior to establishing any treatment or preventive measure. Analysis of the relationships existing between the presence of the variables and classifiers used in moderate and severe sarcopenia revealed that the sarcopenia level using the RBF SVM classifier with Age, HAS, MNA, ECNumber, and Sodium variables has 82′5 accuracy, a 90′2 F1, and 82′8 precision.

Diabetes, also known as chronic illness, is a group of metabolic diseases due to a high level of sugar in the blood over a long period. The risk factor and severity of diabetes can be reduced significantly if the precise early prediction is possible. The robust and accurate prediction of diabetes is highly challenging due to the limited number of labeled data and also the presence of outliers (or missing values) in the diabetes datasets. In this literature, we are proposing a robust framework for diabetes prediction where the outlier rejection, filling the missing values, data standardization, feature selection, K-fold cross-validation, and different Machine Learning (ML) classifiers (k-nearest Neighbour, Decision Trees, Random Forest, AdaBoost, Naive Bayes, and XGBoost) and Multilayer Perceptron (MLP) were employed. The weighted ensembling of different ML models is also proposed, in this literature, to improve the prediction of diabetes where the weights are estimated from the corresponding Area Under ROC Curve (AUC) of the ML model. AUC is chosen as the performance metric, which is then maximized during hyperparameter tuning using the grid search technique. All the experiments, in this literature, were conducted under the same experimental conditions using the Pima Indian Diabetes Dataset. From all the extensive experiments, our proposed ensembling classifier is the best performing classifier with the sensitivity, specificity, false omission rate, diagnostic odds ratio, and AUC as 0.789, 0.934, 0.092, 66.234, and 0.950 respectively which outperforms the state-of-the-art results by 2.00 % in AUC. Our proposed framework for the diabetes prediction outperforms the other methods discussed in the article. It can also provide better results on the same dataset which can lead to better performance in diabetes prediction. Our source code for diabetes prediction is made publicly available.

Obesity is one of the fastest-growing health risks today, especially in countries like India, where lifestyle changes and poor physical activity are becoming common. Obesity causes diseases such as diabetes, heart disease, and high blood pressure. Regular health check-ups sometimes miss early symptoms. There is a need for smarter, continuous monitoring systems. This work proposes a health monitoring solution that uses Wireless Body Area Networks (WBANs) with machine learning (ML) techniques to identify obesity levels and detect irregular health patterns. A Real-world obesity dataset was used, containing personal and habit features like age, weight, height, food habits, physical activity, and family history. These were used to imitate data that wearable sensors may collect. Several ML models were trained and tested-including Support Vector Machine (SVM), K- Nearest Neighbours (KNN). Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and XGBoost - as well as a hybrid SVM+KNN model. The models were evaluated using various performance metrics. Among them, XGBoost gave the best overall accuracy of around 96%, followed by Random Forest and SVM. The hybrid model offered stable and reliable results in borderline cases. By integrating wearable sensors along with ML algorithms, the proposed system supports the detection of obesity-related risks beforehand and provides continuous health status, especially useful in remote and mobile healthcare settings.

Diabetes mellitus is a chronic metabolic disorder that has become a major global health concern due to its increasing prevalence and severe long-term complications. Early prediction and diagnosis of diabetes play a crucial role in preventing disease progression and improving patient outcomes. In recent years, machine learning (ML) techniques have been widely applied to diabetes prediction using clinical and demographic data. This study presents a comprehensive analysis of existing machine learning models used for diabetic prediction. Commonly employed algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbours, Naïve Bayes, and Artificial Neural Networks are examined based on their prediction accuracy, sensitivity, specificity, interpretability, and computational complexity. Publicly available datasets, particularly the Pima Indians Diabetes Dataset, are frequently used for model evaluation. The analysis highlights that ensemble models like Random Forest and Gradient Boosting generally outperform traditional classifiers in terms of accuracy, while simpler models such as Logistic Regression offer better interpretability for clinical decision-making. However, challenges such as data imbalance, overfitting, lack of explainability, and limited real-world clinical validation remain significant. This analytical study provides insights into the strengths and limitations of existing machine learning approaches and identifies research gaps to guide the development of more robust, interpretable, and clinically applicable diabetes prediction systems.

Background: Cardiovascular diseases belong to the top three leading causes of mortality in the Philippines with 17.8 % of the total deaths. Lifestyle-related habits such as alcohol consumption, smoking, poor diet and nutrition, high sedentary behavior, overweight, and obesity have been increasingly implicated in the high rates of heart disease among Filipinos leading to a significant burden to the country's healthcare system. The objective of this study was to predict the presence of heart disease using various machine learning algorithms (support vector machine, naïve Bayes, random forest, logistic regression, decision tree, and adaptive boosting) evaluated on an anonymized publicly available cardiovascular disease dataset. Methodology: Various machine learning algorithms were applied on an anonymized publicly available cardiovascular dataset from a machine learning data repository (IEEE Dataport). A web-based application system named Heart Alert was developed based on the best machine learning model that would predict the risk of developing heart disease. An assessment of the effects of different optimization techniques as to the imputation methods (mean, median, mode, and multiple imputation by chained equations) and as to the feature selection method (recursive feature elimination) on the classification performance of the machine learning algorithms was made. All simulation experiments were implemented via Python 3.8 and its machine learning libraries (Scikit-learn, Keras, Tensorflow, Pandas, Matplotlib, Seaborn, NumPy). Results: The support vector machine without imputation and feature selection obtained the highest performance metrics (90.2% accuracy, 87.7% sensitivity, 93.6% specificity, 94.9% precision, 91.2% F1-score and an area under the receiver operating characteristic curve of 0.902) and was used to implement the heart disease prediction system (Heart Alert). Following very closely were random forest with mean or median imputation and logistic regression with mode imputation, all having no feature selection which also performed well. Conclusion: The performance of the best four machine learning models suggests that for this dataset, imputation technique for missing values may or may not be done. Likewise, recursive feature elimination for feature selection may not apply as all variables seem to be important in heart disease prediction. An early accurate diagnosis leading to prompt intervention efforts is very crucial as it improves the patient's quality of life and diminishes the risk of developing cardiac events.

This study introduces a machine learning (ML) adaptive interface for classifying transparent conductive oxide (TCO) materials by examining their optical characteristics, such as their wavelength, optical density, absorption, and transmission. Six supervised ML models were considered in this study, namely decision tree, random forest, extreme gradient boosting (XGBoost), support vector classifier (SVC), K -nearest neighbors (KNN), and multilayer perceptron (MLP). These models were evaluated using metrics such as accuracy, precision, recall, F1-score, confusion matrices, receiver operating characteristic (ROC) curves, and learning curves. The comparison findings revealed that XGBoost delivered the best performance, achieving an accuracy of $$96.21\%$$ 96.21 % and a perfect area under the curve (AUC) score of 1.00, effectively distinguishing all five sampled TCO classes: aluminum-doped zinc oxide (AZO), fluorine-doped tin oxide (FTO), indium tin oxide (ITO), magnesium-doped zinc oxide (MZO), and zinc oxide (ZnO). These findings reveal ML’s potential for both scientific and industrial integration in material science research. XGBoost, as a high-precision model, may significantly reduce process time and equipment-intensive laboratory characterizations by allowing for the rapid and precise classification of data from optical measurements. This expedites the search for materials suitable for use in photovoltaics, optoelectronics, and display technologies, all of which require high transparency and conductivity. Overall, this work expands the potential for using ML to categorize materials, particularly XGBoost. It provides both scientific understanding and practical applications in innovative substance informatics and industrial quality control.