Detection of mental health conditions like anxiety, depression, and post-traumatic stress disorder (PTSD) at their early stages is crucial for successful treatment interventions. This paper introduces an innovative framework that combines multiple data modalities for identifying psychological disorders. Our approach synthesizes three distinct data sources: audio recordings of speech, text transcriptions of conversations, and clinical measurements including PHQ-8 questionnaire results and patient demographics. At the core of our methodology is a sophisticated attention-driven fusion system that intelligently calibrates the influence of each data stream according to individual patient contexts, generating a comprehensive representation of their mental state. To support clinical understanding, we implement explainable artificial intelligence methodologies (SHAP) that highlight key contributing factors in the classification process and offer healthcare providers meaningful insights into the model's decision-making. The system demonstrated exceptional performance with 95.83% accuracy in differentiating between anxiety, depression, and PTSD cases, surpassing previous approaches that relied on single data types.

Stroke continues to be a major contributor to mortality and disability, highlighting the need for precise early detection systems. This research introduces an innovative combined approach utilizing Fuzzy logic and Long Short-Term Memory (LSTM) networks, enhanced with Explainable AI (XAI) methodologies, to evaluate stroke risk through electroencephalography (EEG) data analysis. Our methodology leverages LSTM networks' temporal pattern recognition capabilities alongside a fuzzy inference framework that converts medical expertise into comprehensible linguistic guidelines. We incorporate XAI tools— SHAP—to overcome deep learning's opacity by providing comprehensive explanations at both global and individual prediction levels. The system architecture processes EEG characteristics and patient clinical information through separate LSTM channels, then integrates these outputs with a fuzzy evaluation system to produce understandable risk assessments. Testing on clinical EEG information yielded remarkable predictive performance: 98.79% accuracy with the Ensemble LSTM and perfect accuracy with the hybrid Fuzzy-ELSTM approach.

This paper presents a novel method to enhance the predictive performance of the Echo State Network (ESN) model by adopting reservoir topology learning. ESNs are a type of Recurrent Neural Network (RNN) that have demonstrated considerable potential in various applications, but they can be challenging to train and optimize due to their random initialization. To improve the learning capabilities of ESNs and enhance their effectiveness in a broad range of predictive tasks, we utilize a structure learning algorithm. The proposed approach modifies the ESN reservoir's connectivity by applying techniques such as reversing, deleting, and adding new connections. We evaluate our proposal performance using both synthetic and real datasets, and our results indicate that it can substantially improve predictive accuracy compared to traditional ESNs.