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@article{168871,
        author = {Prof. Kavita Nagariya and Prof. Babita Kasar},
        title = {Machine Learning Models in Personalized Medicine: A Comprehensive Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {2236-2238},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168871},
        abstract = {Customizing medical treatment according to each patient's unique characteristics, such as genetic composition, lifestyle, and environmental influences, is the aim of personalized medicine. In this area, machine learning (ML) has become a potent tool with advanced models that can process large and complex datasets, which has enormous potential to improve personalized treatment recommendations. In-depth analyses of supervised, unsupervised and reinforcement learning approaches used in customized medicine are provided in this article.
We assess state-of-the-art models and their applications in disease prediction, drug recommendation, and therapy optimization. Additionally, we examine the challenges and limitations related to applying ML in clinical contexts, including worries about model interpretability, data confidentiality, and ethical ramifications.},
        keywords = {Disease Risk Prediction, Drug Recommendation Systems, Machine Learning, Personalized Medicine, Treatment Optimization},
        month = {October},
        }