A physiological signal database of children with different special needs for stress recognition.

This study presents a new dataset AKTIVES for evaluating the methods for stress detection and game reaction using physiological signals. We collected data from 25 children with obstetric brachial plexus injury, dyslexia, and intellectual disabilities, and typically developed children during game therapy. A wristband was used to record physiological data (blood volume pulse (BVP), electrodermal activity (EDA), and skin temperature (ST)). Furthermore, the facial expressions of children were recorded. Three experts watched the children's videos, and physiological data is labeled "Stress/No Stress" and "Reaction/No Reaction", according to the videos. The technical validation supported high-quality signals and showed consistency between the experts.

A comparative analysis of meta-heuristic optimization algorithms for feature selection on ML-based classification of heart-related diseases.

This study aims to use a machine learning (ML)-based enhanced diagnosis and survival model to predict heart disease and survival in heart failure by combining the cuckoo search (CS), flower pollination algorithm (FPA), whale optimization algorithm (WOA), and Harris hawks optimization (HHO) algorithms, which are meta-heuristic feature selection algorithms. To achieve this, experiments are conducted on the Cleveland heart disease dataset and the heart failure dataset collected from the Faisalabad Institute of Cardiology published at UCI. CS, FPA, WOA, and HHO algorithms for feature selection are applied for different population sizes and are realized based on the best fitness values. For the original dataset of heart disease, the maximum prediction F-score of 88% is obtained using K-nearest neighbour (KNN) when compared to logistic regression (LR), support vector machine (SVM), Gaussian Naive Bayes (GNB), and random forest (RF). With the proposed approach, the heart disease prediction F-score of 99.72% is obtained using KNN for population sizes 60 with FPA by selecting eight features. For the original dataset of heart failure, the maximum prediction F-score of 70% is obtained using LR and RF compared to SVM, GNB, and KNN. With the proposed approach, the heart failure prediction F-score of 97.45% is obtained using KNN for population sizes 10 with HHO by selecting five features. Experimental findings show that the applied meta-heuristic algorithms with ML algorithms significantly improve prediction performances compared to performances obtained from the original datasets. The motivation of this paper is to select the most critical and informative feature subset through meta-heuristic algorithms to improve classification accuracy.