Multiple Disease Prediction System Using Machine Learning
DOI:
https://doi.org/10.32628/IJSRSET251253Keywords:
Machine Learning, Django Framework, Disease Prediction, Diabetes, Heart Disease, Parkinson’s Disease, SVMAbstract
The Multiple Disease Prediction System (MDPS) employs machine learning techniques—specifically Support Vector Machines (SVM) and Logistic Regression—to detect various diseases within a single platform. This system is implemented using the Django web framework, offering a user-centric interface for improved accessibility. The primary goal is to support early disease detection and encourage personalized healthcare solutions. The system focuses on predicting diabetes, heart disease, and Parkinson’s disease based on health indicators such as cholesterol levels, heart rate, pulse rate, and blood pressure. Through the use of accurate and efficient ML models, this approach identifies risk factors for multiple illnesses simultaneously. While individual disease prediction models are common, this study presents an integrated solution to predict several diseases at once. The proposed system demonstrates the effectiveness of machine learning in enhancing diagnostic capabilities and promoting better health outcomes.
Downloads
References
R. Katarya and P. Srinivas, “A survey on early-stage heart disease prediction using machine learning techniques,” presented at the International Conference on Electronics and Sustainable Communication Systems (ICESC), 2020, pp. 302–305.
R. P. Poudel, S. Lamichhane, A. Kumar, et al., “Data mining-based risk analysis of type 2 diabetes,” Journal of Diabetes Research, vol. 2018, Article ID 1686023, 2018.
S. Ismaeel, A. Miri, and D. Chourishi, “Application of Extreme Learning Machine in diagnosing cardiovascular diseases,” in IEEE Canada Int. Humanitarian Technology Conference (IHTC), 2015, pp. 1–3.
A. Gavhane, G. Kokkula, I. Pandya, and K. Devadkar, “Heart disease prediction using supervised learning models,” in Proc. 2nd Int. Conf. on Electronics, Communication and Aerospace Technology (ICECA), 2018, pp. 1275–1278.
R. C. Deo, “Integrating machine learning into modern medicine: Opportunities and challenges,” Circulation, vol. 132, no. 20, pp. 1920–1930, 2015.
L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees. Belmont, CA: Wadsworth, 1984.
A. M. Taha and J. Malebary, “An intelligent Parkinson’s disease prediction system using XGBoost,” Healthcare Technology Letters, vol. 7, no. 5, pp. 123–129, 2020.
D. Suresh and G. V. Suma, “Prediction of diabetes using machine learning algorithms,” Materials Today: Proceedings, vol. 33, pp. 4906–4912, 2020.
A. Kumar and A. Tripathi, “A framework for heart disease prediction using data mining techniques,” International Journal of Computer Applications, vol. 145, no. 5, pp. 7–11, 2016.
S. Mohan, C. Thirumalai, and G. Srivastava, “Effective heart disease prediction using hybrid machine learning techniques,” IEEE Access, vol. 7, pp. 81542–81554, 2019.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
