Multiple Disease Prediction Using Machine Learning Techniques

The growing demand for early diagnosis and data-driven clinical support has positioned machine learning as a compelling tool in modern healthcare. Most existing disease prediction systems, however, are built around a single classification model that produces one deterministic output — an approach that inherently fails to reflect the uncertainty and symptomatic overlap commonly encountered in real-world clinical scenarios. This limitation restricts both patients and healthcare providers from meaningfully considering alternative probable conditions during preliminary assessment, and disproportionately affects individuals in underserved regions where timely access to professional medical consultation remains scarce. To address this gap, the proposed system employs a soft-voting ensemble framework that integrates three complementary classifiers — Decision Tree, Naive Bayes, and Random Forest — to generate more balanced and probabilistically informed predictions. Given symptom-based inputs, the system identifies and ranks the top four probable diseases, offering a broader diagnostic perspective than a single-model approach could provide. A real-time web interface allows users to select their symptoms and instantly view ranked predictions, supporting informed preliminary self-assessment and facilitating more meaningful consultations with healthcare professionals.