International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Medical Diagnosis Prediction using Machine Learning

Author(s) Mr. Eshwar Thatikonda, Mr. Himateja Allibadha, Mr. Akhil Nallabai, Prof. Dr. Rajasekhar Nennuri
Country India
Abstract The escalating complexity of medical diagnosis,
characterized by overlapping symptomatology and exponential
growth in healthcare data, necessitates innovative solutions for accurate and timely disease identification. This paper presents the design and implementation of a comprehensive Medical Diagnosis System that leverages machine learning algorithms within a Flask-based web framework to automate disease prediction and optimize healthcare delivery. The system incorporates an ensemble learning approach utilizing Random Forest, Logistic Regression, and Support Vector Machine classifiers, integrated through a Voting Classifier mechanism to enhance diagnostic reliability. Experimental results demonstrate exceptional performance with prediction accuracy ranging from 88% to 95%, with the Voting Classifier achieving peak accuracy of 94.241%. The platform features three synchronized user modules—Administrator, Medical Practitioner, and Patient—enabling seamless symptom input, AI-driven disease forecasting, and intelligent appointment scheduling with specialized physicians. By bridging automated diagnostic capabilities with practical clinical workflow management, the system significantly reduces diagnostic time, minimizes human error, and provides healthcare professionals with an interpretable decision-support tool. This research contributes both empirical validation and architectural framework for deploying intelligent healthcare systems in real-world medical environments.
Keywords Machine Learning , Medical Diagnosis Predic- tion , Healthcare Management , Voting Classifier , Flask Web Application,Supervised Learning,Random Forest , Appointment Scheduling
Published In Volume 8, Issue 1, January-February 2026
Published On 2026-02-04
DOI https://doi.org/10.36948/ijfmr.2026.v08i01.67449
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