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@article{196270,
        author = {Ms. Rutuja Nage and Dr. A.G. Kadu and Ms. Renuka Kalikar and Ms. Maitreyee Patil and Miss. Manjiri Ghate},
        title = {Machine Learning-Based Disease Prediction Using Lifestyle and Health Data},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3120-3126},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196270},
        abstract = {The rising prevalence of lifestyle-related illnesses has encouraged much research attention on the artificial intelligence-based methods of early risk evaluation and individualized healthcare. Lately, there has been a prospective research on the incorporation of machine learning, generative artificial intelligence and recommender systems to examine lifestyle tendencies, forecast diabetic probability, and prescribe preventive measures. The given review is a systematic examination of available literature on the topic of lifestyle-based disease prediction models, with a specific focus on tree-based ensemble models like the Light Gradient Boosting Machine (LightGBM) that have become popular expectation frameworks when working with structured health data. More so, the paper will analyze the future potential of generative AI models to provide individualized preventive advice on nutrition, physical exercise, and behavior change. Symptom-aware doctor recommendation strategies such as keyword-based disease mapping, locationand rating-aware ranking strategies, are also revised. Combining the current research findings, this paper will compare the most applied datasets, algorithms, and system design options and define the main challenges associated with data reliability, interpretability, preservation of privacy, and clinical applicability. The review indicates open research opportunities and gives insights to assist in the creation of effective, ethical, user-friendlier AIdriven preventive health.},
        keywords = {prediction of diseases, generative artificial intelligence, health informatics, machine learning.},
        month = {April},
        }