## 1 Introduction  
Artificial Intelligence (AI) and Machine Learning (ML) are the fastest growing fields in computer science, and are expediting progress in many other research fields [1; 2]. With ever-growing computational power and quantities of data, AI has made significant advancements in numerous areas, including business analytics, speech recognition, system diagnostics/prognostics, and autonomous driving [2]. In particular, the introduction of AI and ML into clinical research has allowed the healthcare sector to make advances in the decision-making process through better detection and prediction of diseases in patients at an early stage ([2] pp. 289-302, [3; 4]). However, despite the excellent solutions offered by the automatic ML (aML) [5] approach, its learning process has become increasingly complex and opaque, limiting its applicability in medical research [1; 2]. In numerous medical fields, current research is confronted with limited data sets, attributed primarily to the rarity of events and the cost of data collection. As a result, the development of intelligent patient-monitoring and disease-predicting techniques is hampered by the generalization of the aML classifiers [6; 7]. Furthermore, many medical problems are characterized by poorly understood causality of events [8]. For example, predicting rare diseases relies on knowledge-driven techniques to pinpoint what is important in the data and incorporate this into the ML model. Against this backdrop, there is an urgent need to enhance the transparency and generalization of ML algorithms. In particular, ML models play a crucial role in identifying singular disorders or diseases in infants. Our previous study applied ML to predict the occurrence of a rare brain injury known as periventricular leukomalacia (PVL) in neonates following congenital heart surgery [9]. Hypoplastic left heart syndrome (HLHS) and transposition of the great arteries (TGA) are two cardiac disorders that usually need surgical intervention in infancy and are linked with increased risks of brain damage [8]. The occurrence of PVL after cardiac surgery has been linked to physiological disorders such as hypoxemia, reduced cerebral blood flow, and low arterial carbon dioxide levels [9; 10]. Although the pathology of PVL is somewhat understood, predicting its occurrence in neonates has thus far remained a challenge since the origins of this rare condition remain to be recognized [9; 11]. Currently, clinicians rely on magnetic resonance imaging (MRI) to diagnose a neonate with PVL. Usually, one MRI is carried out on a patient just before the surgery and one about a week after [12; 13]. By comparing the white brain tissue near the ventricles captured in the MRIs, a clinician can infer if PVL has occurred. Figure 1 illustrates the current approach and compares it with the novel predictive ML method used in our previous study [14]. While the predictive model introduced in our previous studies [14; 15; 16; 17; 18] provided satisfying classification results, it did not explain them. That is, the algorithm had no transparency, providing no indication of which portion of the data was relevant to the performance of the ML algorithm. Hence, in this study, we incorporate a derived step from Active Learning (AL), to determine which part of the data was decisive in making the prediction and, further, to explore the meaning of these data from a physiological perspective [19]. In summary, with a limited amount of data samples, the goal of this study is to develop an interactive ML method that lowers the dimensionality of the final  
Figure 1: Current decision method after 7 days and proposed predictive method (bold) after 12 h.  
disease-predicting model [20] while increasing generalization and overall performance for future unknown data. For this purpose, we first illustrate and explain the shortcomings of the traditional aML approach. Subsequently, we describe our methodology addressing these shortcomings while utilizing interactive ML (iML) [5].