Optimized Predictive Models in Health Care Using Machine Learning

16Developing a Cognitive Learning and Intelligent Data Analysis-Based Framework for Early Disease Detection and Prevention in Younger Adults with Fatigue

Harish Padmanaban P. C.* and Yogesh Kumar Sharma

¹Digital Platform-Site Reliability Engineer, Investment Banking, Bangalore, India

²Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, AP, India

Abstract

Fatigue is a common and often overlooked symptom that can be a sign of underlying health problems. Early detection and prevention of these problems are crucial for improving outcomes, but current methods for identifying and addressing the root causes of fatigue are limited. This research chapter proposes developing a cognitive learning and intelligent data analysis-based framework for early disease detection and prevention in younger adults with fatigue. The proposed framework utilizes clinical data, self-reported symptoms, and objective physical and cognitive function measures to identify patterns and risk factors for various health problems that can cause fatigue. The framework includes a learning module that adapts to the user’s changing health status over time. The results of this research have the potential to significantly improve the accuracy and timeliness of disease detection and prevention in younger adults with fatigue, enabling earlier and more effective interventions.

Keywords: Fatigue detection, AI-based framework, cognitive learning, early disease prevention, ...