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@article{169680,
        author = {Vaishnavi Patekar and Prof.Rutika Shah and Dr.Geetika Narang and Prof.Rupali Maske and Prof.Barkha Shahaji},
        title = {Enhancing Healthcare with Predictive Analytics Using Machine Learning Models},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {1854-1858},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169680},
        abstract = {In recent years, the use of Machine Learning (ML) in healthcare has grown significantly. ML models can use patient data to predict health conditions and suggest proper treatments, improving early diagnosis and treatment efficiency. Predictive analytics in healthcare applies ML to forecast medical events, assess risks, and help with decision-making. This paper reviews how ML is used in areas like disease diagnosis, patient readmission, and personalized treatment plans. We compare models like decision trees, support vector machines (SVM), and neural networks. Additionally, we discuss key challenges such as data privacy, model interpretability, and the practical integration of these models in healthcare. The results show that while ML can improve healthcare outcomes, proper validation and careful integration are crucial for real-world use.},
        keywords = {Predictive analytics, healthcare, machine learning, decision-making, disease diagnosis, personalized medicine, data privacy.},
        month = {November},
        }