Accurate prediction of cholesterol levels is important for early diagnosis and intervention in cardiovascular health, particularly among women, whose lipid metabolism is profoundly influenced by physiological and hormonal factors, such as estrogen fluctuations during menopause. These hormonal changes contribute to increased risks of dyslipidemia, emphasizing the need for sex-specific predictive models. This study presents an explainable machine learning framework using decision treebased models trained on the Study of Women's Health Across the Nation (SWAN) dataset. By applying Recursive Feature Elimination (RFE), we demonstrated that even with just 10 features, it is possible to achieve an accuracy of 0.74 in detecting high cholesterol levels, significantly reducing the burden of data collection. Through decision tree visualization, we identified the substantial influence of the DIABETE10 feature, which received the highest SHAP value and served as the sole criterion for splitting the tree into high and low cholesterol branches. These findings highlight the importance of diabetes in cholesterol prediction, underscoring its clinical relevance. The integration of Shapley Additive Explanation (SHAP) values and decision path visualization enhances model interpretability, providing actionable insights for personalized healthcare and supporting trust in machine learning applications in high-stakes domains.

This paper deals with classifying dog behavior using motion sensors, leveraging a transformer-based Deep Neural Network (DNN) model. Understanding dog behavior is essential for fostering positive relationships between dogs and humans and ensuring their well-being. Traditional methods often fall short in capturing temporal dependencies and efficiently processing highdimensional sensor data. Our proposed architecture, inspired by its success in Natural Language Processing (NLP), utilizes the self-attention mechanism of the transformer to effectively identify relevant features across various time scales, making it ideal for real-time applications. The architecture includes only the encoder part with a classifier's head to output probabilities of dog behavior. We used an open-access dataset focusing on seven different dog behavior, captured by motion sensors on top of the dog's back. Through experimentation and optimization, our model demonstrates superior performance with an impressive accuracy rate of 98.5%, outperforming time-series DNN models. The model's efficiency is further highlighted by its reduced computational complexity, lower latency, and smaller size, making it well-suited for deployment in resource-constrained environments.