International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 1, pp. 153-160, January 2026.
This work is licensed under a Creative Commons Attribution 4.0 International License.
B. Himabindu
A Chaitanya
Y Kumarnadh
T.B. Durga Prasanth
A Kumarsrinivas
Murali Lakshmipathi
Department of CSE, Siddartha Institute of Science and Technology, Puttur, India.
Abstract: Machine learning has the potential to revolutionize healthcare because to the growing availability of medical data and the growing demand for quick, precise diagnosis. The Multi-Disease Detection System presented in this research uses machine learning algorithms to automatically detect and categorize various diseases based on medical imagery and patient data. To guarantee excellent accuracy and dependability across a variety of illness categories, the system incorporates sophisticated preprocessing approaches, feature extraction, and optimal classification models. The program may provide early and reliable illness predictions by identifying intricate patterns and connections in medical records. This helps physicians make decisions more quickly. Additionally, the system has a user-friendly interface that allows users to submit medical data and receive immediate diagnostic results. The goal of this research is to lessen the burden associated with manual diagnostics, improve early detection and support scalable, AI-powered medical solutions.
Keywords: Multi-Disease Detection, Machine Learning, Healthcare Analytics, Medical Data Classification, Disease Prediction.
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