Abstract. The incidence of cancer currently remains one of the most significant problems of modern society. Lung cancer is one of the most lethal forms of oncopathology, which is largely due to its detection in the late stages of the tumor process. The effectiveness of screening and early diagnosis programs has a direct impact on disease prognosis and patient survival rates. In this regard, special attention has been paid in recent years to the development of intelligent expert systems based on deep learning methods, including convolutional neural networks (CNN), transformer architectures and their hybrid solutions. This paper considers the problem of early diagnosis of lung cancer using deep learning neural network models. During the research, models were developed and analyzed using the following machine learning methods: logistic regression, random forest classifier, support vector machine (SVM), extreme tree classifier, XGBoost, CatBoost, gradient boosting and multilayer perceptron (MLP). The indicators of hereditary predisposition, the patient’s gender and smoking experience were used as input factors to the neural network model. The best results were demonstrated by a model based on a multilayer perceptron (MLPClassifier), which reached the maximum value of the ROC-AUC metric, equal to 0.9405, with an accuracy level of over 93%. The obtained indicators indicate the high ability of the model to correctly rank patients by risk groups. The randomForest and SVM algorithms, which took the second position in classification quality, showed comparable results. The developed neural network model makes it possible to assess the likelihood of developing lung cancer, as well as to make recommendations regarding the possible presence of concomitant diseases such as disseminated pulmonary tuberculosis, sarcoidosis, pneumonia and pulmonary fibrosis. In the future, it is planned to expand the functionality of the system by integrating a medical image recognition module, which will create a comprehensive solution for early diagnosis of lung cancer.

Keywords: information systems, artificial intelligence, deep learning methods, intelligent data analysis, intelligent system, databases, convolutional neural network, transformers, hybrid models, explicable AI.