SympTrack: A machine learning approach for predicting heart disease and diabetes

This research investigates the use of machine learning (ML) in predicting multiple diseases, with a primary emphasis on diabetes. By analyzing patient data—including age, lifestyle choices, medical history, and laboratory test results—we developed an efficient predictive model aimed at early diagnosis. Several ML algorithms, such as Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines (SVM), were implemented and evaluated for their accuracy and reliability. Among these, the Random Forest model outperformed others, demonstrating superior accuracy in diabetes prediction. Additionally, the model exhibited the potential to predict other conditions, such as hypertension, highlighting its adaptability for broader healthcare applications. The system is deployed as a user-friendly web application designed to assist both healthcare professionals and patients. It streamlines the early detection process, facilitating timely interventions and improved health management. Regular monitoring and updates ensure that the system remains accurate and relevant in an evolving healthcare landscape. This initiative underscores the transformative impact of ML in the medical field, promoting proactive and personalized patient care. By leveraging advanced technology, it contributes to enhanced health outcomes and increased efficiency in healthcare systems.