健康教育研究開発ジャーナル

Machine Learning in Public Health: A Review of the Problems and Challenges

Abstract

MD Asadullah, Mamunar Rashid, Priyanka Basu, Md Murad Hossain

In recent years Machine learning that has been used for disease diagnosis and prediction in public healthcare sector. It plays an essential role in healthcare and is rapidly being applied to education. It is one of the driving forces in science and technology, but the emergence of big data involves paradigm shifts in the implementation of machine learning techniques from traditional methods. Computers are now well equipped to diagnose many health issues with the availability of large health care datasets and progressions in machine learning techniques. Several machine learning techniques have been used by researchers in public health. Several of these methods, including Support Vector Machines (SVM), Decision Trees (DT), Naïve Bayes (NB), Random Forest (RF) and K-Nearest Neighbors (KNN), are widely used in predictive model design research, resulting in effective and accurate decision-making. The predictive models discussed here are based on different supervised ML techniques as well as various input characteristics and data samples. Therefore, the predictive models can be used to support healthcare professionals and patients globally to improve public health as well as global health. Finally we provide some basic problems and challenges which face the researcher in public health.