TITLE:
Artificial Intelligence and Big Data for Personalized Preventive Healthcare: Predicting Health Risks and Enhancing Patient Adherence
AUTHORS:
Bilkish Nurani, Foysal Kabir, Zakia Sultana Munmun, Ripa Akter
KEYWORDS:
Big Data, Predictive Modeling, Chronic Diseases, Machine Learning, Wearable Devices, Patient Adherence
JOURNAL NAME:
Open Access Library Journal,
Vol.12 No.1,
January
31,
2025
ABSTRACT: Personalized preventive healthcare, powered by Artificial Intelligence (AI) and Big Data analytics, offers a transformative approach to healthcare by tailoring interventions based on an individual’s health risks and lifestyle factors. This study investigates the application of AI models, utilizing Big Data, to design and implement personalized preventive healthcare programs aimed at improving patient outcomes and adherence to health recommendations. The primary objective is to leverage machine learning algorithms to predict health risks, focusing on chronic diseases such as diabetes, hypertension, and cardiovascular diseases (CVDs). Using data from electronic health records (EHRs), wearable devices, and patient demographics, we train models using random forests, logistic regression, and support vector machines (SVMs). These models predict disease risk and generate real-time, personalized intervention strategies. Our results demonstrate that AI-driven models can predict disease onset with high accuracy and provide adaptive, individualized recommendations. Additionally, the study highlights challenges related to data privacy, integration into healthcare systems, and the scalability of such solutions. This research contributes to the growing field of personalized preventive healthcare by showcasing the potential of AI and Big Data in improving disease prediction, enhancing patient engagement, and optimizing healthcare delivery.