Increasing emissions due to urbanization pose a significant threat to human health. Air pollutants such as NO2, PM2.5, and PM10have a well-documented impact on quality of life and mortality risk. Numerous studies are currently investigating the correlation between air pollution and adverse health effects, highlighting the risks associated with exposure to such emissions. In this context, models capable of predicting mortality by leveraging information on pollution and socioeconomic factors are essential for prevention. This work contributes to this field by using Machine Learning (ML) to predict mortality in the Municipality of Taranto (Italy). We propose an eXtreme Gradient Boosting (XGBoost) regression model that uses air pollution levels and socioeconomic data to assess mortality risk over a grid-based map of the examined city. Our study aligns with state-of-the-art findings, further expanding the understanding of the correlation between emissions, economic conditions, and mortality. The proposed model achieves a Root Mean Square Error (RMSE) of 1.61 on the test set, demonstrating the effectiveness of this approach. Additionally, eXplainable Artificial Intelligence (XAI) analysis is conducted using Shapley values to gain insights into the model’s decision-making process and better understand the importance of each feature in predicting mortality risk.

High Flow Nasal Cannula (HFNC) therapy is commonly used in infants hospitalized with bronchiolitis, but its early indication remains challenging. This study proposes an explainable machine learning framework to predict the need for HFNC at admission using real-world clinical data from 109 pediatric patients. After preprocessing, class balancing with SMOTE, and feature selection via RFECV, four classifiers were trained and compared. The Multi-Layer Perceptron (MLP) achieved the best performance, significantly outperforming other models in terms of F1-score. To ensure interpretability, SHAP values were used to identify key predictive features such as SpO2, CRP levels, and hospital stay duration while counterfactual explanations highlighted actionable decision boundaries. The proposed model combines predictive accuracy with clinical transparency, offering a promising tool to support early respiratory management in pediatric bronchiolitis

Cardiovascular disease (CVD) is a general term referring to several heart or blood vessels abnormality. Heart failure (HF), directly associated to (CVD), is a significant global health problem as well as the leading cause of morbidity and mortality. The early detection of this condition is crucial for patient health. Traditional diagnostic methods for HF, such as history taking and physical examination, are often insufficient and require the use of advanced techniques such as Electrocardiogram (ECG). This study aims to extract temporal and morphological features from (ECG) signals and compare different Machine Learning (ML) classification models to enable rapid diagnosis and provide interpretable predictions. Specifically, we propose a Light Gradient Boosting (LGBM) model that can discriminate Normal Sinus Rhythm (NSR) and Arrhythmia (ARR) with a high accuracy of 0.99, achieving a Precision of 1.00, Recall of 0.99, and f1-score of 0.99 in the (NSR) class, and Precision of 0.99, Recall of 1.00, and f1-score of 0.99 in the (ARR) class, respectively. In addition, eXplainable Artificial Intelligence (XAI) analysis is performed to explain the model predictions.

Brain-computer interfaces are widely used to control machines using Electroencephalography (EEG) signals. Several low-cost electroencephalographs are available on the market that achieves good-quality EEG signals. One of the most intriguing issues for developing biofeedback systems is classifying users' emotional states using EEG signals and Machine Learning (ML) methods. In our study, we propose a novel ML-based biofeedback tool using a BCI to detect two different users' mental states: Focus, and Relaxation. We compared several ML algorithms achieving an average accuracy on the Test Set of 0.90 by using SVM. Finally, we propose a prototype for music generation according to the classification output that could be adopted in neurorehabilitation scenarios.

Mild Cognitive Impairment (MCI) is a syndrome charac-terized by cognitive impairment that is greater than expected for a subject's age and level of education. Nevertheless, it does not interfere with daily activity. Prevalence in epidemiological and population-based studies ranges from 3% to 19% in adults older than 65 years. A very interesting approach in this area is related to the identification of an Artificial Intelligence (Al)-based model and a subset of relevant features to predict the MCI clinical outcome. In our study, we propose a Pareto-optimality-based approach to identify the best model for predicting MCI. In fact, the best model achieves an Accuracy and Recall on Yes MCI of 71 % and 80% respectively. With this approach, it is possible to select the best model in order to predict Yes MCI (highest risk class). Our study presents a new best model selection approach that can be applied in identifying the best model that can be applied in various disease classification problems.

Brain-Computer Interfaces allow controlling machines through signals coming from Electroencephalography (EEG) analysis. Nowadays, there are several cheap electroencephalographs available on the market that guarantee good quality EEG signals. A very interesting approach in this area is related to detecting the emotional states of a user through the analysis of her EEG signal. In our study, we tried to detect the emotional polarity (Valence), the state of emotional excitement (Arousal), and the level of emotion control (Dominance). Through metric interpolation and Russell’s circumplex model, it is possible to characterize and define the current emotional state of the user who wears the device. Our study presents a prototype of an EEG-based emotion recognizer that provides the user’s emotional state exploitable as bio-feedback.

Machine Learning could help the healthcare industry manage huge amounts of data and discover hidden trends and patterns that could help us better understand disease development and treatment. The goal is to define a Neural Network model (NN) to classify physical frailty in aging cohort to identify the frail food and clinical profile. In a 1, 929 older cohort from Southern Italy, the Food Frequency Questionnaire (FFQ) and clinical data were collected with blood tests. A NN was built with a hyperparameter tuning technique using accuracy as a performance parameter to select the best model. Confusion matrices, Garson and Olden’s variable importance were evaluated. Older age, female gender, high BMI, and high blood pressure were associated with physical frailty. In frail subjects, the lipid profile and RBC levels were significantly lower than their counterpart. On the contrary, serum levels of interleukin-6 and CRP were higher in the frail group. Frail subjects show higher consumption of spaghetti soup, pecorino cheese, fennel and chocolate, while a lower consumption of ham. The NN model has a respective training and testing accuracy of 86.49% and 85.77%. NN performs well on the train. The test dataset makes few mistakes and can predict healthy subjects with high specificity. According to Garson’s method, age, gender, foods rich in fats, and smoking habits are essential in predicting the frailty condition. In contrast, Olden’s method underlined the higher consumption of legumes and unrefined cereals.