CDSS is widely used in certain medical fields, including the diagnoses of heart diseases [7; 8; 9; 10], kidney diseases [11; 12], diabetes [13; 14], cancer [15; 16], Alzheimer's disease [17], etc. CDSS are also applied in the field of geriatrics, where they are used successfully to assess signs of depression with an average accuracy of 95% [18] and diagnose dementia with a balanced accuracy of 82.3% [19], with an accuracy of 95.2% [20], and with an accuracy varying from 73% to 92% for various cases as presented in [21]. All the papers we analyze apply a variety of artificial intelligence techniques, such as neural networks, support vector machines, Bayes classifiers, or fuzzy logic and fuzzy cognitive maps. Fuzzy logic-based methods are quite popular for CDSS implementation, especially when uncertain input data should be processed as presented in [12; 22]. The authors of [12] demonstrated that their proposed fuzzy DSS outperformed the physicians in diagnosing kidney stones and kidney infection and achieved 95% and 85% accuracy, respectively, while physicians achieved only 70% and 65% accuracy in the same cases. The authors of [22] proposed an automatic computer-aided diagnosis system based on a fuzzy cognitive map (FCM) to determine the grade of celiac disease and achieved a classification accuracy of 91.01%. As stated by [23], fuzzy modeling has a huge potential for the development of advanced explainable AI (XAI) systems, in which the results of the solution can be understood by humans. Fuzzy-based XAI enables the development of decision-making rules based on the knowledge of medical experts and is capable of assessing inaccurate and subjective input data. XAI-based decision support systems are suitable for the diagnosis and evaluation of patient health risks when the decision is determined by many different inter-dependent factors. Furthermore, XAI-based CDSS allows verifying the reason behind the decision suggested by the system and comparing this result with the physician's reasoning. Therefore, the physician can be sure that the decision proposed by the system is the right one, especially in cases where the physician has a different initial opinion. The research presented in this work contributes to the field of the application of artificial intelligence in CDSS systems. This paper describes the application of explainable artificial intelligence (XAI) for the implementation of the CDSS to diagnose geriatric diseases and syndromes related to malnutrition. Studies related to the diagnosis of nutritional disorders and the identification of related syndromes using artificial intelligence and CDSS are not found in the scientific literature, except our initial study [6], which proposes a theoretical XAI-based decision support model for nutrition-related geriatric disorders. This paper presents the real-world experimental evaluation of the theoretical XAI-based CDSS model [6] with real patient data. The aim of this study is to develop the software implementation of web-based CDSS and to evaluate the effectiveness of the proposed system by comparing its diagnoses with those of medical experts. This tool would be useful for the inexperienced physician (resident geriatricians, family physicians, general practitioners, etc.) to pay attention to nutrition-related geriatric syndromes and perform a detailed examination of nutrition-related disorders.