There are many characteristics that can be studied by taking heart rate measurements, of which a very common one is heart rate variability signal (HRV), since it has been found to be directly related to health [35], mental stress [36], cognitive functions [37], and psychosomatic state [38]. Autonomic dysregulation is a biomarker for ASD and ADHD. Specifically, assessment using HRV can distinguish sensory reactivity in ASD children from that found in typically developed children [39; 31]. Furthermore, ADHD can be assessed using HRV to distinguish measurements regarding sustained attention and emotional and behavioral regulation deficits seen in ADHD, and it may help to define the pathophysiology of the disorder [40; 41]. Machine learning (ML) is a subset of AI and a rapidly evolving field of study that aims to establish high-quality prediction models using search strategies, deep learning, and computational analysis to enable machines to learn to make autonomous decisions and improve their performance at specific tasks [42]. There are several uses for ML in health and healthcare [43; 44; 45; 46; 47; 12; 48; 14; 48]. The way we approach disease/disorder screening, diagnosis, and treatment may change as a result; for example, ML algorithms can examine patient data to spot trends and forecast the course of diseases/disorders. Supervised ML for classification is a type of machine learning where a model is trained to predict a categorical output variable. Metrics such as accuracy, error rate, precision, and recall can be used to evaluate a classification model's performance [39; 49]. A good classification model should have high accuracy, precision, and recall, but the optimal values may depend on the specific problem being addressed. For instance, early detection of type 2 diabetes and its complications has been identified from electronically collected data using ML and deep learning techniques [50; 51]. Further, towards individualized treatment plans, ML algorithms can examine patient data, including genetic data and medical history improving treatment results [52; 53]. Wearable technology and sensor data can be analyzed by ML algorithms to track patient health and spot early disease symptoms [54; 55]. In relation to this, a soft computing approach of predictive fuzzy cognitive maps has been employed successfully to represent human reasoning and to derive conclusions and decisions in a way that is human-like for a Medical Decision Support System [48]. This system was intended for medical education, employing a scenario-based learning approach to safely explore extensive \"what-if\" scenarios in case studies and prepare for dealing with critical adversity [48]. Additionally, a sub-band morphological operation method has also been used successfully to detect cerebral aneurysms [56] and convolutional neural networks have been employed for the classification of leukocytes categories and leukemia prediction [57]. Furthermore, wearable electroencephalogram (EEG) recorders and Brain Computer Interface software have been proposed to aid in the assessment of alcohol-related brain waves [58]. More specifically, calculated spectral and statistical properties were used for classification, and Grammatical Evolution was applied. The suggested approach reported high accuracy results (89.95%), and thus, it was suited for direct drivers' mental state evaluation for road safety and accident avoidance in a future in-vehicle smart system. Further, for the hemiplegia type classification among patients and healthy individuals, an automatic feature selection and building method based on grammatical evolution (GE) for radial basis function (RBF) networks was presented [59]. Using an accelerometer sensor dataset, this approach was put to the test using four different classification techniques: RBF network, multi-layer perceptron (MLP) trained using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) training algorithm, support vector machine (SVM), and GenClass, a GE-based parallel tool for data classification.