AI-Powered Disease Diagnostic Predictive Model using Neural Networks

Abstract

The digital transformation of healthcare has seen an increasing reliance on Artificial Intelligence (AI) and Machine Learning (ML) technologies to support diagnostic and decision-making processes. In a world where access to healthcare services remains uneven—especially in remote or economically underdeveloped regions—technology is being leveraged to fill the accessibility gap. The rise of intelligent applications has not only enhanced the accuracy of diagnostics but has also enabled faster, scalable, and cost-effective solutions. This research introduces a lightweight neural network web application that identifies patterns within symptoms to suggest a probable disease. With a main focus on accessibility, affordability, and adaptability, the core of this system is a deep learning model trained on a dataset consisting of 5,000 symptom-disease mappings covering 41 unique diseases. The model with regularisation has achieved an outstanding 96.54% accuracy. The neural network with users, whether healthcare professionals or general individuals, can interact with the application to input symptoms and receive a disease prediction within seconds. This serves as an initial assessment tool, prompting users to seek professional advice if necessary. The system is designed to be lightweight using TensorFlow Lite, making it deployable even on low-end devices. It is hosted online to ensure ease of access and is free of cost, promoting inclusivity. The incorporation of a feedback mechanism—where users can correct wrong predictions—adds another layer of intelligence by laying the groundwork for reinforcement-based learning in future versions.