Improving COPD Readmission Prediction with Optimized Machine Learning - Rajasekaran S; Shaik Mastan Val (Journal of Artificial Intelligence and System Modelling)
Millions of people worldwide suffer from Chronic Obstructive Pulmonary Disease (COPD), a common chronic respiratory illness. Readmissions can frequently be avoided when patients at high risk are treated promptly and with increased monitoring. Therefore, it becomes vital to identify hospital readmission risk early on. This research undertook a comparative analysis between 2 distinct models, Extra Trees Classification (ETC) and Adaptive-Boost Learning Classifier (ADAC), each augmented with School Based Optimization (SBO) and Flying Foxes Optimization (FFO) techniques for hyperparameter optimization. Their efficacy was evaluated in forecasting COPD using various performance metrics, including accuracy, precision, recall, and F1-score. Independent evaluators were engaged to scrutinize the model outcomes to ensure impartial assessment. These original models were further refined through hybridization with the aforementioned optimizers, resulting in ETSB (ETC + SBO), ETFF (ETC + FFO), ADSB (ADAC + SBO), and ADFF (ADAC + FFO). In the testing phase, the ETFF model demonstrated superior performance with an accuracy of 0.8917, while the ADAC model exhibited comparatively weaker performance with an accuracy of 0.7500. Similarly, in terms of precision, the ETFF model outperformed others with a value of 0.8940, whereas the ADAC model showed the weakest precision performance with a value of 0.7610.