Aug 2026· International Journal for Research in Applied Science and Engineering Technology· 0 citations
TL;DR
A diagnostic support system based on a unified web platform that classifies patients according to the risks of developing three diseases based on regularly collected clinical or audio data using classical supervised learning algorithms is presented.
Abstract
Chronic non-communicable diseases such as diabetes mellitus, cardiovascular disease, and Parkinson’s disease are
significant causes of morbidity and mortality. Screening is limited by the accessibility to medical experts and the cost of
appropriate diagnostic tests. In this paper, we present a diagnostic support system based on a unified web platform that classifies
patients according to the risks of developing three diseases based on regularly collected clinical or audio data using classical
supervised learning algorithms. For the diabetes mellitus classification task (Pima Indians dataset, 8 features) and Parkinson’s
disease detection (Oxford voice recording dataset, 22 features), Support Vector Machine classifiers with linear kernels were
trained. A Logistic Regression classifier was used for predicting occurrences of heart disease (UCI Cleveland dataset, 13
features). The training results in the form of the classifiers were serialised with Pickle and implemented as a web application
with the Flask framework and MySQL database software that differiates between the administrators’ and patients’ interfaces.
With the stratified 80/20 holdout validation method, the system demonstrated accuracy of 77.3%, 85.2%, and 87.2% for the
diabetes mellitus, heart disease, and Parkinson’s disease detection tasks, respectively. The paper describes mathematical
algorithms used for machine learning in detail and presents computational specifics of the Support Vector Machine
implementation for the Pima Indians dataset as an example. Additionally, the accuracy, precision, recall, and F1-score metrics
were calculated based on the confusion matrices for each of the tasks.
An intelligent machine learning-based web application that predicts the presence or absence of heart disease in patients based on 13 key medical parameters such as age, cholesterol, blood pressure, chest pain type, and maximum heart rate is presented.
Patchikaru Ganesh Patchikaru Ganesh, K. T. K. K. K. Tulasi Krishna Kumar· International Scientific Jou...· 0 citations
Diabetes is a chronic metabolic disease characterized by high blood glucose levels that can lead to severe complications affecting various organs. The increasing prevalence of diabetes requires an accurate and efficient diagnostic approach to support early detection and medical decision-making. The development of machine learning technology provides opportunities to assist disease diagnosis by analyzing patient health data and identifying patterns associated with diabetes conditions. This study aims to compare the accuracy performance of the Naive Bayes and CatBoost algorithms in diagnosing diabetes using the confusion matrix evaluation method. The research method consists of several stages, including data collection, data preprocessing, model selection, model training, and model evaluation. The dataset used was obtained from Kaggle, consisting of 768 records with nine attributes, including pregnancies, glucose level, blood pressure, skin thickness, insulin, body mass index, diabetes pedigree function, age, and outcome classification. The models were trained using 80% training data and 20% testing data. The evaluation results showed that the Naive Bayes algorithm achieved an accuracy of 79%, while the CatBoost algorithm obtained a higher accuracy of 81%. These findings indicate that CatBoost provides better classification performance compared to Naive Bayes for diabetes diagnosis in this dataset. Therefore, CatBoost can be considered a more effective machine learning approach to support diabetes prediction. Future research is recommended to utilize larger datasets, optimize model parameters, and explore other advanced algorithms to improve diagnostic accuracy.
Basyach Aryodito Tranggono Heryanto, Onny Marleen· Journal of social research· 0 citations
This paper proposes a machine learning–based multi-disease prediction system that integrates disease-specific classifiers within a unified, real-time clinical decision-support platform. The framework employs Support Vector Machine (RBF) for diabetes prediction, Support Vector Machine (linear) for heart disease, Decision Tree for chronic kidney disease (CKD), and Logistic Regression for cancer prediction, with each classifier selected according to the statistical characteristics of its respective dataset. The system is implemented using a Streamlit-based web interface, enabling efficient real-time prediction with interpretable outputs. Experimental evaluation demonstrates strong predictive performance, achieving accuracies ranging from 85.71% to 94.30% and AUC-ROC values between 0.91 and 0.97 across the four disease modules, representing a 7.2 percentage-point improvement over comparable unified prediction systems reported in the literature. The modular architecture provides scalability, low computational complexity, and rapid inference, making it suitable for pre-diagnostic screening in clinical environments. The proposed framework offers an effective and practical solution for early chronic disease detection while supporting future expansion. Planned enhancements include the integration of deep learning models for medical imaging and electronic health records, Explainable Artificial Intelligence (XAI) techniques such as SHAP and LIME, wearable and IoT-based continuous health monitoring, federated learning for privacy-preserving distributed model training, and prospective clinical validation through hospital information system integration. These developments are expected to improve prediction accuracy, interpretability, scalability, and clinical applicability for next-generation intelligent healthcare systems.
Chandrasekar.M, Y. S, Adithiyaa K.B· 2026 International Conferenc...· 0 citations
Most current clinical decision support with health care and machine learning are developed for a single disease and are therefore not integrated into the workflows when the patient needs to be assessed for multiple diseases. We present a single prediction architecture aimed at solving diabetes, heart disease and Parkinson's in a single integrated pipeline to overcome this limitation. However, less than 1,300 original records are provided by the three publicly available UCI benchmark sources, which is not enough to allow stable multi-model training, so we created a 50,000 -sample research dataset using merely statistical augmentation with a distribution-aware approach inside the training fold, without using real patients; this is not a 50,000 sample set of distinct real patients, it was only created for the purpose of controlled benchmarking. All the performance statistics mentioned in this document are taken from the original benchmark records that were held out, and not modified during the augmentation process. Four of these classifiers (Support Vector Machine (SVM), Decision Tree (DT), Artificial Neural Network (ANN) and Random Forest (RF)) were trained and evaluated using a common and strictly controlled experimental setup. For the three tasks, RF outperformed all the other algorithms with accuracies of $\mathbf{9 1. 9 \%,\ 8 9. 3 \%}$, and $\mathbf{9 6. 2 \%}$, and with a mean AUC-ROC of 0.955. Repeated stratified splits and paired significance tests ($p<0.05$) show that these numbers are constant between runs. One path that can be taken toward deployment is demonstrated with a Flask based web prototype. The numbers listed here are representative of a research prototype only and should not be used as evidence of clinical readiness.
Avala Phanindra, Mandadapu VenkataKrishnaChaitanya, Tirumalasetti Narasimha Rao· 2026 Third International Con...· 0 citations
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.
Experimental results demonstrate that Machine Learning techniques can effectively predict disease occurrence with high accuracy, thereby assisting healthcare professionals in early diagnosis and treatment planning.
Sunidhi, Mothe Rahul, Mr. D. Pramod Kumar et al.· International Journal for Re...· 0 citations
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