Full Text PDF
ESTD Year: 2017 | Impact Factor (2026): 8.7
DOI Prefix: 10.47001/IRJIET
ESTD Year: 2017 | Impact Factor (2026): 8.7
DOI Prefix: 10.47001/IRJIET
Vol 10 No 6 (2026): Volume 10, Issue 6, June 2026 | Pages: 33-45
International Research Journal of Innovations in Engineering and Technology
OPEN ACCESS | Research Article | Published Date: 06-06-2026
Enterprise The increasing prevalence of heart disease and the growing dependency on hospital-based diagnosis have created major challenges in providing timely and accessible healthcare services, especially in rural and underserved regions. Retrieving accurate health assessments from patient lifestyle data and medical parameters remains difficult due to limited medical accessibility, delayed diagnosis, and the lack of intelligent preventive healthcare systems. Traditional diagnosis methods often rely on clinical tests, expert evaluation, and hospital infrastructure, which may be time-consuming, costly, and inaccessible for many individuals. To address these limitations, this paper proposes a Machine Learning-based Heart Disease Prediction System designed to provide accurate, accessible, and real-time heart disease risk assessment without immediate hospital dependency.
The proposed system integrates multiple machine learning algorithms including Logistic Regression, Random Forest, Support Vector Machine (SVM), and Artificial Neural Networks (ANN) to improve prediction accuracy and risk classification. The framework utilizes clinical datasets such as the UCI Heart Disease Dataset and Kaggle datasets containing attributes including age, blood pressure, cholesterol level, chest pain type, and heart rate. In addition, the system incorporates preprocessing, probability-based risk classification, and explainable prediction mechanisms to improve reliability and user understanding. The backend is implemented using FastAPI for efficient request handling and prediction processing, while the frontend is developed using ReactJS to provide a user-friendly interface for real-time health assessment. Experimental evaluation demonstrates improved prediction accuracy, efficient response generation, and reliable risk classification, making the proposed system suitable for scalable preventive healthcare and early heart disease detection applications.
Heart Disease Prediction, Machine Learning, Logistic Regression, Random Forest, SVM, ANN, FastAPI, ReactJS, Risk Classification, Preventive Healthcare, Early Detection.
Adamala Hruthika Reddy, Etcherla Pranathi, M Mamatha, & Y Pavan Narasimha Rao. (2026). Heart Sense: Early Heart Disease Detection without Hospital Dependency. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(6), 33-45. Article DOI https://doi.org/10.47001/IRJIET/2026.106003
This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
S. Palaniappan and R. Awang, “Prediction of Heart Disease using Naive Bayes Algorithm,” IEEE International Conference, 2022.
R. Chitra and V. Seenivasagam, “Heart Disease Prediction using Decision Tree Classification,” International Journal of Computer Applications, 2022.
K. Srinivas, G. Raghavendra Rao, and A. Govardhan, “Machine Learning Techniques for Heart Disease Prediction,” International Journal of Advanced Research, 2023.
M. Gudadhe, K. Wankhade, and S. Dongre, “Heart Disease Prediction using Artificial Neural Networks,” IEEE Transactions on Healthcare Informatics, 2023.
Framingham Heart Study Researchers, “Risk Prediction of Heart Disease using Logistic Regression Models,” Medical Journal of Cardiology, 2023.
A.Kumar, R. Singh, and P. Sharma, “Hybrid Machine Learning Model for Heart Disease Prediction,” Springer Journal of Medical Systems, 2023.
P. Anooj, “Support Vector Machine based Heart Disease Prediction System,” International Conference on Data Science and Healthcare, 2024.
J. Thomas and T. Princy, “K-Nearest Neighbors Algorithm for Heart Disease Detection,” Journal of Data Mining and Knowledge Discovery, 2024.
S. Mohan, C. Thirumalai, and G. Srivastava, “Deep Learning Approaches for Heart Disease Prediction,” IEEE Access, 2024.
U. R. Acharya, S. L. Fernandes, and J. E. W. Koh, “Ensemble Learning Techniques for Heart Disease Prediction,” Knowledge-Based Systems Journal, 2024.
S. Raj, R. Karthikeyan, and P. Kumar, “IoT-Based Heart Disease Monitoring and Prediction System,” IEEE Internet of Things Journal, 2025.
H. Yan, Y. Wang, and L. Zhang, “Data Mining Techniques for Early Heart Disease Prediction,” International Journal of Medical Informatics, 2025.
A.Sharma and S. Rizvi, “Comparative Study of Machine Learning Algorithms for Heart Disease Prediction,” International Conference on Artificial Intelligence Applications, 2025.
R. Tomar, P. Verma, and S. Gupta, “Artificial Neural Network with Feature Selection for Heart Disease Diagnosis,” Procedia Computer Science, 2025.