Editorial for the Special Issue on “Machine Learning in Healthcare and Biomedical Application”

ABSTRACT

The authors demonstrated the reliability of the use of cluster analysis in discovering intra- and inter-diagnostic heterogeneity in the cognitive profile of Parkinsonism patients, and, more importantly, showed how to transform a ML approach into a decision support tool for use in a clinical setting [5]. The proposed method [7] overcomes the problems of the time-consuming conventional approaches used for the identification and quantification of malaria parasitemia thanks to transfer learning, which is applied on digital images, with a Faster Regional Convolutional Neural Network (Faster R-CNN) and Single Shot Multibox Detector (SSD). [...]demand to increase the interpretability of ML findings has emerged [2,4,5], as the recent growing interest of the scientific community in Explainable Artificial Intelligence (XAI) demonstrates [8].