Six supervised learning models were developed and compared for diabetes prediction using a dataset and compared for diabetes prediction using a 100k patients records with eight clinical features including gender, age, hypertension, smoking history, heart disease, BMI, HbA1c level, and blood glucose level.
Diabetes mellitus is a common type of metabolic illness that is very common worldwide, and in most cases, it results in serious effects like heart disease, kidney disease, and blindness. Proper and early diagnosis of diabetes is essential to intervene on time and have better patient outcomes. Machine learning (ML) paradigms provide effective predictive modeling solutions to healthcare, but most of the current literature is limited due to imbalanced datasets, using a single training test split, and limited model interpretability, which diminish their clinical usability. This research paper has introduced a powerful and explainable ML model to predict diabetes based on the Pima Indians Diabetes Dataset acquired via Kaggle, which contains 768 patients with eight clinical variables and a binary response. To counter the class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is used to create natural synthetic samples of the minority diabetic group that facilitate balanced learning without degrading the correlations between the features. Four classifiers, including Logistic Regression, Naive Bayes, AdaBoost, and XG Boost, are trained and tested. The stratified 10-fold cross-validation is used to provide a stable and generalizable model performance, as opposed to using only one data split. The measurement criteria are accuracy, precision, recall, and F1-score, especially for the minority diabetic class. The interpretation of the model is improved by the use of logistic regression coefficients and SHAP (SHapley Additive exPlanations) values, as they allow transparent identification of clinical features that are critical to making predictions. The results of the experiment show that the suggested framework attains an overall accuracy of approximately 94% on an unseen test set, with strong precision and recall of the minority class, thus proving that the combination of class balancing, cross-validation, and explainable ML results in the outcomes of reliable and clinically credible predictions. All performance results are evaluated on an untouched original test set, while SMOTE is applied strictly within cross-validation folds to prevent data leakage. Unlike many existing studies, the proposed framework ensures leakage-free validation, robust cross-validation, and integrated interpretability for clinically meaningful prediction. Although synthetic sampling improves minority class learning, the model is evaluated carefully to ensure generalization on real-world data. This paper indicates that a rigorously conducted methodology and interpretability in machine learning development are crucial in creating machine learning solutions in healthcare decision support, which is the pathway to real applications in diabetes risk assessment.
T. Khan, M. Saeed, Majid Hussain et al.· Scientific Reports· 0 citations
: Diabetes has become a severe challenge to global public health: in 2023, there are 537 million cases worldwide (expected to rise to 783 million by 2045), with annual medical expenses exceeding $727 billion. Furthermore, 30% to 50% of patients are undiagnosed and asymptomatic, highlighting the urgent need for precise predictive models. The advantages of machine learning are significant: for instance, the random forest model combining HbA1c and FLI achieved an AUC of 0.874, while the cross-population model from the THIN database achieved an AUC ranging from 0.907 to 0.925. The integration of lifestyle data through CATBoost revealed a U-shaped risk association with sleep duration, and the accuracy of the multi-source hybrid model exceeded 98%, confirming the value of multimodal integration. However, current research faces challenges including data heterogeneity, insufficient external validation, poor interpretability of deep learning, and difficulties in multimodal integration. In the future, it is essential to establish a standardized validation framework, develop interpretable algorithms, integrate wearable non-invasive markers, and implement Bayesian racial modeling to promote early screening and personalized intervention, thereby revolutionizing the clinical prevention paradigm.
Dingnan Wu· Proceedings of the 3rd Inter...· 0 citations
Diabetes has become a health problem worldwide. It often goes unnoticed until it causes health issues. Finding diabetes early using a lot of health and personal data can help reduce the diseases impact and healthcare costs. This study proposes a machine learning system for diabetes prediction. This system uses techniques to prepare data select important features handle unequal class distributions and combine multiple models. It is designed to process types of data from Electronic Health Records (EHRs) lifestyle factors and clinical measurements efficiently. Multiple machine learning models, for example tree-based classifiers, simple linear models and combined models are. Tested. Cross-validation is used to ensure the models are reliable and can be scaled up. The prediction of diabetes mellitus is based on identifying factors, so the importance analysis of characteristics is used to find the most influential predictors of diabetes. Oversampling of medical data involves the use of oversampling to overcome the problem of class distributions. The findings indicate that the given approach is more accurate, precise, possesses higher recall and F1-score, as well as ROC-AUC, compared to other models. This developed system offers an understandable solution for assessing diabetes risk early. It can be used in healthcare screening systems and clinical decision-support platforms for diabetes mellitus.
Thatikonda Krishna Kalyan Gupta, Oruganti Yashwanth Reddy, I. S et al.· 2026 International Conferenc...· 0 citations
Cardiovascular disease, as a highly prevalent chronic condition, has shown a continuously rising incidence in China and now ranks as the leading cause of death among both urban and rural residents. Mainstream Cardiovascular disease risk prediction models have mostly been developed based on European and American populations, which do not align well with the physical characteristics and disease patterns of the Chinese population. Moreover, traditional statistical methods have inherent limitations, further restricting the clinical applicability of these models. To address this, the present study constructed a Cardiovascular disease risk prediction model tailored to the Chinese population using machine learning algorithms based on the China Health and Retirement Longitudinal Study database. The dataset was split into a training set and a test set at a ratio of 7:3. Seven algorithms were employed for parallel modeling, and multi-dimensional performance comparisons were conducted. The study found that, in addition to traditional risk factors such as blood pressure and blood glucose, sleep indicators—including nap duration and nighttime sleep duration—were also important influencing factors for Cardiovascular disease. The comparative results demonstrated that the LightGBM model achieved the best predictive performance, with an AUC of 0.828, a recall of 0.717, and an F1 score of 0.568. The integration of SHapley Additive exPlanations further validated the internal logic and rationality of the model. This model can assist clinicians in risk assessment, thereby effectively improving the accuracy and efficiency of cardiovascular disease prediction.
Longfa Chu, Jing Lin, Zekai Li et al.· 2026 IEEE 27th China Confere...· 0 citations
Diabetes mellitus is a long-term metabolic disorder that typically goes unrecognised until it has already caused significant harm, making timely identification a critical clinical priority. Unfortunately, conventional diagnostic approaches frequently fall short in detecting the disease before it progresses, particularly within busy healthcare settings. To tackle this gap, the current study introduces a machine learning-based framework enhanced with explainability, built around a structured data preparation process that handles categorical encoding, numerical scaling, and minority class oversampling through the SMOTE technique. A pair of classification models, Logistic Regression and Random Forest, are trained, tested, and directly compared to assess their diagnostic reliability. The findings reveal that Random Forest consistently delivers stronger results, reaching a classification accuracy of 98%, which reflects its capacity to learn intricate relationships within real-world clinical data. To move beyond raw performance, SHAP analysis is integrated to shed light on how individual patient attributes shape each prediction outcome. The resulting system strikes a meaningful balance between diagnostic accuracy and model interpretability, positioning it as a trustworthy tool for assisting medical professionals in data-driven clinical decision-making.
N. J, Deekshitha U, Kavya V· International Journal of Sci...· 0 citations
Hypertension, commonly known as high blood pressure, is a major risk factor for cardiovascular diseases and premature mortality worldwide. Early detection and prevention are critical in reducing its health impact. This study explores the application of machine learning (ML) techniques to predict the likelihood of hypertension in individuals using clinical and demographic data. A variety of supervised learning algorithms, including Logistic Regression, Random Forest, Support Vector Machines, and Gradient Boosting, were evaluated for their predictive performance [1]. The dataset was preprocessed through feature selection, normalization, and handling of missing values to improve model accuracy.[2] Performance metrics such as accuracy, precision, recall, F1-score, and AUC-ROC were used to assess the models [4]. The results demonstrate that ML models can effectively identify individuals at high risk of hypertension, offering a valuable tool for early intervention and personalized healthcare [5]. This approach underscores the potential of artificial intelligence in supporting public health efforts and enhancing clinical decision-making.
Key words: Logistic Regression, Random Forest, Support Vector Machines, and Gradient Boosting.
G. Vamsi, K. Bhargavi· International Scientific Jou...· 0 citations