Abstract
Oncological diseases are one of the deadliest diseases in the modern world. Early diagnosis of these diseases helps to increase the life expectancy of patients. The development of measures and programs for the early diagnosis of oncological diseases is one of the urgent problems today. The use of intelligent systems and artificial intelligence methods in the diagnosis of cancer is an important aspect in this matter. In such cases, expert systems or decision support systems are mainly used. This paper examines the early diagnosis of lung cancer using a protocol survey and taking into account regional factors. The research is conducted for residents of the former Semipalatinsk nuclear test site. As the tests carried out have an impact on the health of the citizens of this region to this day. The region is one of the five regions with the highest incidence of oncological diseases and mortality rates. An analysis of the existing expert systems was carried out. Neural networks for decision support systems were used as the basis of the model. Each parameter of the model is assigned a weight, relative to which the significance is calculated and preliminary instructions are given on the further actions of the interviewed patient. As a result, the factors that most strongly influence the incidence of lung cancer were identified.