A Comprehensive Review of Machine Learning Approaches for Predictive Analytics in Healthcare Diagnosis and Clinical Decision-making
Atta Yaw Agyeman *
D Jarvis College of Computing and Digital Media, DePaul University, Chicago, United States of America.
Lois Azupwah
University Clinic, University of Energy and Natural Resources, Sunyani, Ghana
Samuel Gbli Tetteh
D Jarvis College of Computing and Digital Media, DePaul University, Chicago, United States of America.
Stephen Kwasi Adjei
Center for Information Systems and Technology, Claremont Graduate University, California, United States of America.
Ankrah Prince Twumasi
Department of Computer and Electrical Engineering, University of Energy and Natural Resources, Sunyani, Ghana.
Sofo Mohammed-Nurudeen
Department of Computer and Electrical Engineering, University of Energy and Natural Resources, Sunyani, Ghana.
*Author to whom correspondence should be addressed.
Abstract
This study explores the application of machine learning (ML) methodologies to improve clinical decision-making, with a specific emphasis on diagnostic and prognostic modeling in the healthcare domain. It underscores the utility of ML algorithms in processing large-scale, heterogeneous medical datasets for disease classification, risk prediction, and pattern recognition. Despite their potential, several challenges hinder the full integration of ML in clinical settings, including model interpretability, class imbalance (particularly in datasets with low disease prevalence), and the necessity of earning clinicians’ trust through reliable and transparent predictions. The research implements traditional ML techniques to support prognostic assessment in life-threatening cardiovascular conditions, addressing key preprocessing tasks such as feature engineering, class rebalancing, and threshold optimization for risk stratification. Furthermore, the study incorporates a patient-centered framework by modeling patient preferences using supervised learning algorithms to predict individualized treatment choices. This dual approach—combining technological precision with human-centric considerations—aims to assist clinicians in delivering personalized care, thereby enhancing decision quality, patient engagement, and overall satisfaction with healthcare outcomes.
Keywords: Machine learning, clinical decision-making, prognostic models, cardiovascular diseases, patient-centered care, predictive analytics, data preprocessing, feature selection, risk assessment, patient preferences, healthcare communication, health literacy