This article is dedicated to the necessity of developing predictive models based on data for making quality decisions in management systems. Methods based on time scries in the process of data analysis and forecasting are presented. The importance of time series analysis in shaping management strategy, its impact on accuracy and efficiency, as well as the analysis of mathematical models and algorithms necessary to enhance the reliability of results, are discussed.

The paper presents a comparative analysis of machine learning algorithms applied for the early diagnosis of oncological diseases. The paper examines algorithms such as Random Forest, XGBoost, AdaBoost, and others, tested on various clinical tasks including cervical, lung, and skin cancer. Special attention is given to ensemble methods, which demonstrated the highest accuracy, particularly the Random Forest algorithm. The study emphasizes the versatility of the methods, their adaptability to heterogeneous medical data, and their potential for developing intelligent clinical decision support systems.