Reduced Number of Parameters for Predicting Post-Stroke Activities of Daily Living Using Machine Learning Algorithms on Initiating Rehabilitation

Abstract

The estimation of the Barthel Index scale (BI) is a significant method for measuring the performance of Activities Daily Living (ADL), where the prediction of ADL is crucial for providing rehabilitation care management and recovery for patients after stroke, therefore in this paper, nine various Machine Learning (ML) algorithms were implemented in a medical dataset contains 776 records from 313 patients 208 of them are men: 208 and 150 are women with multiple features collected from them for predicting and classifying the BI status as clinical decision support for determining the ADL of post-stroke patients. Meanwhile, we have applied feature selection using the chi-squared test to reduce the number of features in the dataset. The results showed that the Decision Tree (DT), XGBoost (XGB), and AdaBoost (ADB) classifiers performed the highest performance achieved with 100% correctness in terms of accuracy, sensitivity, specificity, error, and Area Under Curve (AUC) on both the full and reduced features datasets. Povzetek: V prispevku je predlagana metodologija za zmanjšanje števila parametrov za napovedovanje primernih aktivnosti po možganski kapi.

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A Machine Learning Challenge: Detection of Cardiac Amyloidosis Based on Bi-Atrial and Right Ventricular Strain and Cardiac Function

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