Web-Based Symptom Checker and Disease Detection Using ML Algorithms

International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 3, pp. 404-412, March 2026.

https://doi.org/10.58482/ijersem.v2i3.51

This work is licensed under a Creative Commons Attribution 4.0 International License.

Web-Based Symptom Checker and Disease Detection Using ML Algorithms

E Murali

P Dhanush

G Bharath

N Ganga Swetha

D Arun Teja

Department of CSE, Siddharth Institute of Engineering & Technology, Puttur, AP, India.

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Abstract: 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 false-negative 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 Naïve 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.

Keywords: Clinical Decision Support Systems, Disease Prediction, Ensemble Learning, Telemedicine, Web-Based Healthcare.

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This article is part of Volume 2, Issue 3 (March 2026)