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.