Copyright © 2026 Authors retain the copyright of this article. This article 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.

@article{196949,
        author = {Jignasa Patel and Dr. Kruti Dangarwala and Jalpa Patel},
        title = {Intelligent Healthcare: AI And Machine Learning Approach in Chronic Disease Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {6629-6638},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196949},
        abstract = {The prediction of human diseases constitutes an essential component of human existence. The process of disease prediction entails estimating the likelihood of patients’ illness by analyzing the interplay the symptoms of patient. Vigilantly patient health monitoring status and pertinent data during the initialization of assessment can significantly aid medical practitioners in effectively addressing the patient’s health concerns.  Recent progressions in Machine Learning (ML) approaches have resulted in considerable improvement in the identification and prediction of health crises, demography of disease, disease progression, and immunological responses among others. The objective of this article is to represent a comprehensive overview of various algorithms like support vector machine, Naïve Bayes, K- nearest Neighbor, Random Forest, Logistic Regression and decision tree of machine learning that help in the detection of various diseases. This study systematically reviews a multitude of prior research endeavours that have used various machine learning algorithms for the identification of diverse diseases within the domain of healthcare. It is particularly critical to diagnose individuals with chronic diseases at the early possible stage. This review has concentrated on evaluating the application of ML techniques for predicting chronic diseases, such as heart disease, cancer, and liver ailments. In this review, numerous previous studies focusing on chronic disease prediction were examined, and we assessed and compared the accuracy of various ML algorithms in detecting breast cancer.},
        keywords = {Artificial intelligence, Machine Learning, Healthcare, K-Nearest Neighbor, Random Forest, Naïve Bayes, Logistic Regression, Decision Tree, Chronic Diseases Prediction.},
        month = {April},
        }