@article {10.3844/jcssp.2025.1251.1265,
article_type = {journal},
title = {IntelliHealth: A Machine Learning Driven Disease Detectionand Diet recommendation System},
author = {Wadhwan, Ankita and Chawla, Priyanka and Kaur, Sandeep and Mittal, Usha},
volume = {21},
number = {6},
year = {2025},
month = {May},
pages = {1251-1265},
doi = {10.3844/jcssp.2025.1251.1265},
url = {https://thescipub.com/abstract/jcssp.2025.1251.1265},
abstract = {People all around the world are afflicted with various ailments. An accurate diagnosis can lower the risk of significant health problems developing, but an inaccurate diagnosis could have adverse implications. In this study, an ensemble-based strategy "IntelliHealth" has been presented to identify disorders of the thyroid, liver, and breast cancer using three machine learning (ML) approaches consisting of Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The datasets for this research work are acquired from Kaggle. The experimental results show that the ML based ensemble model provides the highest level of disease prediction accuracy. This model is 93% accurate for liver, 99% accurate for breast cancer, and 100% accurate for diabetes and thyroid. Also, in this study, a web-based application is developed that uses proposed ensemble for quickly predicting diseases based on the patient's profile and recommends a diet plan.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}