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.

Machine Learning, Disease Prediction, Soft Voting Ensemble, Healthcare Decision Support.

1. P. Hamsa Gayathri and S. Vigneshwaran, “Symptoms Based Disease Prediction Using Machine Learning Techniques,” in 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), IEEE, 2021. [Google Scholar] [Crossref]

2. Palak Mahajan, Shahadat Uddin, Farshid Hajati, and Mohammad Ali Moni, “Ensemble Learning for Disease Prediction: A Review,” published by Victoria University and The University of Sydney. [Google Scholar] [Crossref]

3. Oltean Anisia Veronica, Ioan Daniel Pop, and Adriana Mihaela Coroiu, “Medical Chatbot for Disease Prediction Using Machine Learning and Symptom Analysis,” Babeș-Bolyai University, Department of Computer Science, Romania. [Google Scholar] [Crossref]

4. Divyansh Nishad, Anshika Mishra, and Nidhi Goyal, “Symptom-Based Disease Prediction Using Machine Learning,” in 2024 14th International Conference on Computing Communication and Networking Technologies, IEEE, 2024. [Google Scholar] [Crossref]

5. Ridham Sood and Virat Sharma, “Symptom Based Disease Prediction Using Machine Learning,” in International Conference on Computing, Communication and Automation, IEEE, 2018. [Google Scholar] [Crossref]

6. Manikanta Sirigineedi, Matta Eswar Surya Manikanta Kumar, Rali Surya Prakash, Velagala Pavan Kumar Reddy, and Poojitha Tirunagari, “Symptom-Based Disease Prediction: A Machine Learning Approach,” Journal of Artificial Intelligence, Machine Learning and Neural Network, 2024. [Google Scholar] [Crossref]

7. Priya Mishra, Uday Singh Kushwaha, and Shraddha Singh, “Disease Prediction Using Machine Learning: A Comparative Study of Classification Algorithms for Symptom-Based Diagnosis,” International Journal for Research in Applied Science and Engineering Technology, 2025. [Google Scholar] [Crossref]

8. D. Ajmera, T. N. Pandey, S. Singh, S. Pal, S. Vyas, and C. K. Nayak, “Early-Stage Disease Prediction from Various Symptoms Using Machine Learning Models,” EAI Endorsed Transactions on Internet of Things, 2024. [Google Scholar] [Crossref]

9. Vaishnavi K, Hanamant R Jakaraddi, and Priyanka G N, “A Machine Learning Approach for Disease Prediction Based on Age, Lifestyle Habits, and Symptom Analysis,” International Journal of Latest Technology in Engineering Management & Applied Science, 2025. [Google Scholar] [Crossref]

10. Md Saiful, S. M. Zobayed, and Shayma Sultana, “Symptom-Based Disease Classification Using ML Algorithms,” in IEEE Computer Society Bangladesh Symposium, 2024. [Google Scholar] [Crossref]

11. Weicheng Sun, Ping Zhang, Zilin Wang, and Dongxu Li, “Machine Learning-Based Prediction of Cardiovascular Diseases,” ICCK Transactions on Internet of Things, 2024. [Google Scholar] [Crossref]

12. Soniya Pasi, Sheshang Degadwala, and Malini Joshi, “Symptom-Based Classification of Common Syndromes Using Machine Learning: A Review,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2026. [Google Scholar] [Crossref]

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