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@article{183452,
        author = {Rishika Shinde and Prof. Avneesh Dubey},
        title = {Understanding Chronic Risk Through Daily Habits: A Scalable ML-Driven Simulation Platform},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {1540-1552},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183452},
        abstract = {With an ever-changing routine that navigates the complexities of a modern life, we need new ways to use data for people’s better health. With this system, we explain and allow individuals to explore personal health risk predictions built similarly to advanced health risk models that are used by experts. We are unique since we cover a wide range of lifestyle aspects — such as age, occupation, eating habits, rest, mental state, and exposure to chemicals — something missing from the usual risk models. With what-if simulation, users can change their behaviours and note how their risk levels go up or down in real time. Ensuring that data processing is complete can be done by using categorical encoding, scaling, and mapping each feature clearly. Apart from forecasting, the tool helps people improve their health literacy and confidence in themselves by showing how to act on the results. This way, our project establishes an original approach that combines data science with empowering users, which will benefit upcoming research on preventive healthcare. Categorical encoding, scaling, and explicit feature mapping can all be used to ensure that data processing is finished. In addition to forecasting, the tool demonstrates how to act on the outcomes, which helps people become more health literate and self-assured. In this sense, our effort creates a novel method that blends data science with user empowerment, which can help future studies on preventative healthcare.},
        keywords = {healthcare; electronic health records; machine learning; patient monitoring; medical data; predictive analytics},
        month = {August},
        }