AI-Powered Symptom-Based Disease Prediction

Abstract

Symptom-based diagnosis, Disease prediction, Machine learning, Healthcare analytics, Predictive modeling, Medical decision support system, Classification algorithms, Data mining in healthcare, Feature selection, Symptom–disease correlation, Clinical data analysis, Patient symptom dataset, Artificial intelligence in healthcare, Supervised learning, Diagnosiautomation, Machine learning models (SVM, Random Forest, Naïve Bayes),Healthcare prediction system, Data preprocessing, Early disease detection, Health informatics

Introduction

The text discusses the use of machine learning (ML) for symptom-based disease prediction to address the challenges of timely and accurate diagnosis in healthcare. Traditional diagnosis can be slow and error-prone, especially in high-demand settings, impacting vulnerable populations like the elderly, children, and those with limited access to care. ML models can analyze large datasets of symptoms to identify patterns and predict diseases quickly, improving diagnosis accuracy, enabling early treatment, and reducing healthcare costs.

The study reviews prior work on disease prediction using algorithms such as Naive Bayes, SVM, Decision Trees, Random Forest, and ensemble methods across illnesses including diabetes, heart disease, malaria, dengue, and tuberculosis. Data sources include structured and unstructured medical records, and preprocessing enhances model performance.

In the methodology, a dataset with 132 symptom features and training/testing samples was used. Various ML models—KNN, Gaussian Naive Bayes, SVM, Decision Tree, Random Forest, and XGBoost—were trained to predict diseases from symptoms. All models achieved perfect performance metrics (accuracy, precision, recall, F1-score = 1.0) on the available data, demonstrating strong potential for disease prediction.

The study highlights that symptom-based ML models can be integrated into web applications for public use, enabling early self-assessment, awareness, and timely medical intervention. While results are promising, models require testing on more diverse and real-world data, along with updates to incorporate new healthcare information. Overall, ML-based disease prediction offers a transformative approach to healthcare, improving diagnostic efficiency and outcomes globally.

References

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Copyright

Copyright © 2025 Dr. Sumalatha Bandari, Mr. Siddharth Raipure , Ms. Sakshi Varma, Ms. Sakshi Kate, Mr. Sameer Gahlot , Ms. Srushti Karlewar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.