Authors : Dr. M. Raja; M. Lakshman; M. Arun Teja; M. Vinay; M. Vishnu Vardhan

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/38spuedx

Scribd : https://tinyurl.com/2pd74rjx

DOI : https://doi.org/10.38124/ijisrt/25apr2147

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Abstract : A personalized healthcare recommendation system uses artificial intelligence (AI) and machine learning (ML) to provide tailored health suggestions based on an individual’s medical history, symptoms, genetic data, and real-time health conditions. It collects data from sources such as wearable devices, electronic health records (EHRs), and mobile health applications, analyzing patterns to predict potential health risks and offer preventive measures. With the integration of big data and the Internet of Things (IoT), these systems enhance diagnosis accuracy, improve chronic disease management, and increase patient engagement. They assist doctors in making data-driven decisions, reducing hospital visits, and lowering healthcare costs. However, security and privacy concerns remain critical, requiring encryption, blockchain technology, and strict data-sharing policies to protect sensitive patient information. Despite these benefits, challenges like data bias, system reliability, and ethical considerations persist. Future advancements in AI and deep learning will help address these issues, making personalized healthcare systems more reliable, accessible, and effective in delivering improved medical services.

Keywords : A personalized healthcare recommendation system uses artificial intelligence (AI) and machine learning (ML) to provide tailored health suggestions based on an individual’s medical history, symptoms, genetic data, and real-time health conditions. It collects data from sources such as wearable devices, electronic health records (EHRs), and mobile health applications, analyzing patterns to predict potential health risks and offer preventive measures. With the integration of big data and the Internet of Things (IoT), these systems enhance diagnosis accuracy, improve chronic disease management, and increase patient engagement. They assist doctors in making data-driven decisions, reducing hospital visits, and lowering healthcare costs. However, security and privacy concerns remain critical, requiring encryption, blockchain technology, and strict data-sharing policies to protect sensitive patient information. Despite these benefits, challenges like data bias, system reliability, and ethical considerations persist. Future advancements in AI and deep learning will help address these issues, making personalized healthcare systems more reliable, accessible, and effective in delivering improved medical services.

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