Assessing the Efficacy of ML Techniques for Forecasting Healthcare Consumer Readmission: A Comparative Analysis of Risk Factors and Healthcare Interventions

This study evaluates the efficacy of machine learning techniques for predicting patient readmissions, focusing on the comparative analysis of various algorithms in identifying risk factors and the potential for optimizing healthcare interventions. Healthcare professionals now prioritize preventing avoidable hospital readmissions because of the substantial effects it has on patient outcomes and healthcare expenditures. Machine learning is a viable solution to this problem because of its capacity to analyse enormous volumes of data and identify significant patterns. In order to determine how well machine learning models, including Random Forest, Support Vector Machine (SVM), Logistic Regression, and Neural Networks, predict readmission risks based on variables like age, comorbidities, length of hospital stay, and past treatment history, the study evaluates these models' accuracy and performance. Data was collected from electronic health records (EHRs) encompassing a wide range of clinical, demographic, and social factors. The results demonstrated that machine learning algorithms could significantly improve the prediction of readmissions, with Random Forest outperforming other techniques from a precision and recall perspective. Moreover, the study highlights the potential of machine learning in recommending timely, personalized healthcare interventions, such as tailored discharge planning and patient monitoring, which could reduce the likelihood of readmission. The findings highlight the revolutionary potential of machine learning in enhancing predictive healthcare systems, improving patient outcomes, and optimizing resource utilization.