Multimodal Disease Prediction System with Integrated Medical Assessment

Author(s)	Ms. Gorrepotu Snehitha, Ms. M Satya Sai Vahini, Ms. Ippili Aswitha, Mr. Chinthada Chandu, Mr. Devandla Narasimham
Country	India
Abstract	It is important to detect life-threatening diseases in time for proper diagnosis and treatment. Pneumonia, brain tumors, and heart diseases are conditions that can be controlled if proper treatment is adopted for them. Earlier, in the automated models, the performance of the system using traditional algorithms such as Support Vector Machine, Decision Tree, Logistic Regression, and Convolutional Neural Networks was found to be 86%, considering less feature learning. However, in this paper, the authors have proposed a new system known as a Multimodal Disease Prediction System with Integrated Medical Assessment. The images of the patients, such as MRI, X-ray, and ECG, are processed using EfficientNetV2 and Random Forest algorithms. The experimental outcome of the proposed system has proved to perform with 91.32% accuracy, thus proving to be more reliable for diagnosis.
Keywords	Pneumonia Detection, Brain Tumor, Heart Disease Prediction, EfficientNetV2, Random Forest, Diagnostic Decision Support
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-03

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International Journal For Multidisciplinary Research