Defining distinctive areas of the physical properties of rocks plays an important role in reservoir evaluation and hydrocarbon production as core data are challenging to obtain from all wells. In this work, we study the evaluation of lithofacies values using the machine learning algorithms in the determination of classification from various well log data of Kazakhstan and Norway. We also use the wavelet-transformed data in machine learning algorithms to identify geological properties from the well log data. Numerical results are presented for the multiple oil and gas reservoir data which contain more than 90 released wells from Norway and 10 wells from the Kazakhstan field. We have compared the the machine learning algorithms including KNN, Decision Tree, Random Forest, XGBoost, and LightGBM. The evaluation of the model score is conducted by using metrics such as accuracy, Hamming loss, and penalty matrix. In addition, the influence of the dataset features on the prediction is investigated using the machine learning algorithms. The result of research shows that the Random Forest model has the best score among considered algorithms. In addition, the results are consistent with outcome of the SHapley Additive exPlanations (SHAP) framework.

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Medicine is one of the rich source of data, generating and storing massive data, begin from description of clinical symptoms and end by dierent types of biochemical data and images from devices. Manual search and detecting biomedical patterns is complicate task from massive data. Data mining can improve the process of detecting patterns. Stomach disorders are most common disorders that aect over 60% of human population. In this work, the classication performance of four non linear supervised learning algorithms i.e. Logit, K-Nearest Neighbour, XGBoost and LightGBM for ve types of stomach disorders are compared and discussed. The objectives of this research is to nd trends of using or improvements of machine learning algorithms for detecting symptoms of stomach disorders, to research problems of using machine learning algorithms for detecting stomach disorders. Bayesian optimization is considered to nd optimal hyper parameters in the algorithms, which is faster than the grid search method. Results of the research shows algorithms that base on gradient boosting technique (XGBoost and LightGBM) get better accuracy more 95% on test dataset. For diagnostic and conrmation of diseases need to improve accuracy, in the article we propose to use optimization methods for accuracy improvement with using machine learning algorithms. Keywords: Stomach disorder • machine learning algorithm • decision support system • Bayesian optimization.