Web-Based Symptom Checker and Disease Detection Using ML Algorithms

Limited healthcare accessibility in remote and underserved regions remains a major global challenge, often resulting in delayed diagnosis and adverse patient outcomes.Conventional diagnostic procedures are resource-intensive and depend heavily on the availability of specialized medical professionals.To address this challenge, a web-based Clinical Decision Support System (CDSS) is proposed for predicting disease probabilities based on user-reported symptoms.Unlike traditional approaches that rely on single-classifier models, the proposed system adopts a heterogeneous ensemble learning architecture integrating Random Forest, Support Vector Machines (SVM), and Gradient Boosting classifiers to improve diagnostic accuracy and reduce falsenegative rates.The framework processes symptom inputs through a scalable web interface and maps them to structured feature representations using a standardized medical dataset to identify nonlinear relationships between symptoms and diseases.Experimental evaluation demonstrates that the ensemble model achieves a classification accuracy of 96.2%, outperforming baseline algorithms such as Nave Bayes (84.5%) and Decision Trees (87.1%).In addition to prediction capability, the system provides precautionary recommendations, enabling its use as an effective first-line screening tool.The results confirm the feasibility of deploying lightweight, high-accuracy machine learning models on web platforms to support accessible preliminary medical diagnosis.