Artificial Intelligence-Based Clinical Risk Prediction Using Ensemble Machine Learning Algorithms

Abstract

Risk prediction tools are becoming increasingly popular tools to assist in making clinical decisions. The models, however, are typically trained on data from general patient cohorts and may not be representative of and applicable to targeted patient cohorts when used in practice. This study overcame these obstacles by developing and evaluating a clinical risk prediction model using the MIMIC-III clinical dataset and an Artificial Neural Network (ANN). The suggested method accounts for all possible preprocessing steps—including normalization, encoding, managing missing values, and data balancing using SMOTE-ENN—to increase the dependability of the predictions. With an F1-Score (F1) of 96.9%, an accuracy (ACC) of 98.6%), a precision (PRE) of 97%, and a recall (REC) of 96.5%, the ANN model has high predictive capacity and can capture the complicated interaction between the clinical variables. The outcomes demonstrate that the suggested ANN architecture can accurately and consistently assess clinical risk, which in turn allows for the early identification of high-risk patients and aids healthcare providers in making data-informed therapeutic decisions. Because it can provide trustworthy prediction models from complex health data, the proposed method has great potential for clinical real-time application. It can also help develop smarter healthcare decision-support systems and enhance patient monitoring methods.