The field of machine learning is rapidly advancing in various research areas, including disease prediction and inventory optimization. Recent studies have focused on developing innovative models and frameworks that leverage machine learning techniques to improve the accuracy and efficiency of disease prediction and inventory management. Notable advancements include the use of hybrid architectures combining convolutional neural networks and long short-term memory layers for effective feature extraction and sequential learning, as well as the integration of explainable machine learning methods to provide insights into model predictions. Additionally, researchers have explored the application of machine learning algorithms to predict stroke risk and heart disease, with a focus on addressing methodological challenges such as class imbalance and missing data. The use of data-driven methods for inventory optimization has also been investigated, with studies evaluating the effectiveness of different algorithms and models in supermarket contexts. Overall, these developments demonstrate the potential of machine learning to drive advancements in disease prediction and inventory optimization, and highlight the need for further research in these areas to fully realize the benefits of these technologies. Some noteworthy papers in this regard include: The DNet model, which combines CNN and LSTM layers to achieve high accuracy in diabetes prediction. The GBDTSVM model, which leverages a Gradient Boosting Decision Tree and Support Vector Machine to predict snoRNA-disease associations with high accuracy.