Jul 2026· International Journal for Research in Applied Science and Engineering Technology· 0 citations
TL;DR
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.
Abstract
Healthcare is one of the most significant application domains of Machine Learning, where early disease prediction
can help improve patient outcomes and support clinical decision-making. This dissertation presents a Diabetes Prediction and
Analysis System Using Machine Learning that predicts the likelihood of a disease based on various patient health parameters
and medical records. The system utilizes a healthcare dataset containing attributes such as glucose level, blood pressure, body
mass index (BMI), insulin level, age, and other relevant medical factors.
The collected data is pre-processed through missing value handling, feature normalization, and data partitioning to enhance
prediction performance. Multiple Machine Learning algorithms, including Support Vector Machine (SVM), K-Nearest
Neighbours (KNN), Decision Tree, and Random Forest, are employed to develop predictive models. The performance of these
models is evaluated using metrics such as accuracy, precision, recall, F1-score, and confusion matrix. Comparative analysis is
carried out to identify the most suitable algorithm for disease prediction.
The implementation of the proposed system is carried out in MATLAB, utilizing its Machine Learning and data analysis tools
for model training, testing, performance evaluation, and result visualization.
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. The proposed system provides an
efficient and reliable approach for disease prediction and analysis, contributing to improved healthcare management and
decision support.
This study presents a Multiple Disease Prediction System (MDPS) that predicts the likelihood of various diseases using patient health information and medical datasets and achieves satisfactory predictive performance across multiple disease categories.
Prachi Kumari· Journal of Intelligent Syste...· 0 citations
This study addresses the challenge of delayed diagnosis of diabetes, a condition that often leads to severe complications if not detected early. The primary objective is to evaluate and compare the performance of three machine learning classifiers XGBoost, Support Vector Machine (SVM), and Random Forest for early diabetes prediction using clinical and lifestyle data. The study utilizes the Diabetes Health Indicators dataset, which includes features such as body mass index (BMI), blood pressure, cholesterol levels, and physical activity. The dataset was sourced from a publicly available repository and preprocessed through handling missing values, feature scaling, and encoding categorical variables. The models were trained on the processed dataset and evaluated using accuracy, precision, recall, and F1-score metrics, alongside exploratory data analysis to understand feature relationships. Results show that all three models performed effectively, with XGBoost achieving the highest accuracy of 85.11%, followed by SVM at 84.82%, and Random Forest at 83.16%. These findings highlight the strength of ensemble and boosting techniques in handling complex health data and accurately predicting diabetes risk. In conclusion, machine learning models demonstrate strong potential for supporting early diabetes diagnosis and improving clinical decision-making. It is recommended that healthcare systems adopt XGBoost-based predictive models in clinical decision support tools for early screening, while future studies should validate these models using real-world clinical data to enhance reliability and generalizability.
Idehen Emmanuel Imafidon, Chikere Obinna Munachiso, Dominic Evans Onyebuchi et al.· International Journal of Sci...· 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
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
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
Stroke is one of the leading causes of death and long-term disability worldwide, making early prediction and preventive healthcare extremely important. Traditional methods of stroke diagnosis mainly depend on clinical observations and medical expertise, which may sometimes delay timely identification of high-risk patients. To address this challenge, the proposed project “Stroke Prediction Using Ensemble Learning” introduces an intelligent healthcare prediction system that utilizes machine learning techniques to predict the likelihood of stroke occurrence based on patient health parameters. The system analyzes important medical attributes such as age, hypertension, heart disease, glucose level, body mass index (BMI), smoking status, and other lifestyle-related factors to provide accurate stroke risk assessment. The proposed model employs Ensemble Learning techniques, which combine multiple machine learning algorithms to improve prediction accuracy and robustness. Algorithms such as Random Forest, Gradient Boosting, AdaBoost, and Voting Classifier are integrated to enhance overall system performance and reduce the limitations of individual models. Data preprocessing techniques including missing value handling, normalization, feature selection, and class balancing are applied to improve the quality of the dataset and ensure reliable predictions. The trained ensemble model is capable of identifying complex patterns in medical data and classifying patients into stroke-risk categories with high efficiency. The system is designed with a user-friendly interface that allows healthcare professionals or users to input patient information and obtain instant prediction results. Performance evaluation metrics such as accuracy, precision, recall, F1-score, and confusion matrix are used to assess the effectiveness of the proposed model. By providing early stroke risk prediction, the system can support doctors in decision-making, promote preventive healthcare, and reduce mortality rates associated with stroke. The proposed approach demonstrates how ensemble learning and artificial intelligence can contribute significantly to modern healthcare systems by enabling faster, more accurate, and cost-effective medical predictions.
Bhagyashri Patil, Priyadarshini C Patil, Soumya M A et al.· International journal of com...· 0 citations