Integrating Smart Health Informatics with Community Health Education: A Predictive Diagnosis Framework for Rural U.S. Communities

Sadia Islam Nilima; Afia Fairooz Tasnim; Mst Masuma Akter Semi; Rukshanda Rahman; Sumaiya Yeasmin; Sweety Rani Dhar; Mohammad Zahidul Alam; Md Bayzid Kamal

doi:10.63332/joph.v3i3.2589

Authors

Sadia Islam Nilima Department of Business Administration, International American University, Los Angeles, CA 90010, USA
Afia Fairooz Tasnim Department of Public Health, California State University, Long Beach, CA 90840, USA
Mst Masuma Akter Semi Department of Business Administration, International American University, Los Angeles, CA 90010, USA
Rukshanda Rahman Department of Computer Science, Westcliff University, Irvine, CA 92614, USA
Sumaiya Yeasmin Department of Psychology, St. Francis College, NY 11201
Sweety Rani Dhar Department of Business Administration, International American University, Los Angeles, CA 90010, USA
Mohammad Zahidul Alam Department of Information Technology, Westcliff University, Irvine, CA 92614, USA
Md Bayzid Kamal Department of Business Analytics, Brooklyn College, CUNY (City University of New York)

DOI:

https://doi.org/10.63332/joph.v3i3.2589

Keywords:

Smart healthcare framework, Chronic disease management, Deep learning model, Remote patient monitoring, Vital signs prediction, Rural healthcare optimization

Abstract

This study presents a smart predictive healthcare framework tailored to support individuals in the United States living with chronic conditions, especially those receiving care at home. The framework incorporates a deep learning model that analyzes large volumes of patient data, including vital signs, physical activity, medication usage, and symptoms. These data are collected through ambient assisted living technologies. The model is part of an intelligent module that operates at the patient’s location to deliver accurate health status predictions and personalized care recommendations. The framework was tested using data from patients with chronic blood pressure conditions, collected every 15 minutes over one year. The proposed model achieved a prediction accuracy of approximately 97.6% % outperforming a standard baseline model by nearly 6%. Additionally, improvements in identifying critical health events were observed, with the F score increasing by 9% for hypertensive, 26% for hypotensive, and 10% for normotensive cases. These results demonstrate the model’s effectiveness in detecting early warning signs and enhancing the management of chronic diseases. The framework shows strong potential for improving healthcare access and reducing emergency risks in rural and underserved communities across the United States.