Web App-based Multiple Diseases Detection Using Machine Learning: A Novel Approach

The increasing prevalence of various diseases calls for the development of efficient and accurate disease detection systems. In this study, we propose a systematic approach for multiple disease detection using machine-learning techniques using a web-based application. The proposed method utilizes a dataset of medical data and employs pre-processing, feature extraction, and classification techniques to detect and diagnose diseases. The system's performance was evaluated using various metrics, including accuracy, sensitivity, and specificity, which showed promising results. The proposed method offers an accessible and cost-effective solution for disease diagnosis, allowing for early detection and timely treatment. Furthermore, the developed web-based application can be deployed on a cloud platform, enabling easy access and scalability. This proposed approach can significantly improve disease detection and diagnosis, thus enhancing healthcare outcomes.