Enhancing Health Data Prediction with Software Engineering and Machine Learning: An Application for Health Systems

Abstract

In recent times, machine learning has garnered significant attention as a cutting-edge area of research. As a result, our study focuses on exploring the fascinating intersection between software engineering and machine learning within the realm of health systems. We have introduced an innovative framework dedicated to health informatics. This framework is structured around four crucial modules: software, machine learning, machine learning algorithms, and health informatics data. By utilizing the proposed methodology, we have effectively organized tasks within this framework. The primary goal is to provide researchers and developers with a fresh perspective on health informatics software, incorporating engineering principles. As a result, developers are equipped with a comprehensive roadmap to design health applications, complete with system functions and software implementations. To power the proposed approach, we employ principal component analysis (PCA) for feature extraction and reduction. This technique plays a pivotal role in simplifying complex datasets and facilitating efficient data analysis. Additionally, the proposed model leverages the extreme learning machine (ELM) for prediction problems, contributing to the accurate forecasting of health-related outcomes. In our experimentation, we employed the Indian Diabetes dataset to conduct simulations. Our proposed ELM demonstrated exceptional performance, surpassing the state-of-the-art approaches in terms of predictive accuracy and efficiency.