A Decision Model Based on Artificial Intelligence for Disease Prediction And Patient Treatment Process Planning

Abstract

Purpose: The aim of this study is to determine which patients are at risk of lung cancer with a sample of 1000 people living in China and a data set consisting of 20 variables. In this direction, it is aimed to take preventive measures for individuals at risk of developing lung cancer, to protect human health, and to contribute to the effective and efficient use of resources. In this way, the model developed in the planning of preventive and therapeutic activities of professionals working in the health sector will be exemplary as a decision model.

Methodology: A linear regression model was developed and applied to predict individuals at high risk of developing lung cancer using machine learning on the Microsoft Azure Machine Learning Studio platform.

Findings: Lung cancer risk of the patients in the sample was predicted with the machine learning application. It was determined that these predictions made with machine learning gave an effective result and provided efficient policies. The developed model and application example is proposed as a modern, valid and artificial intelligence-based decision-making model for professionals.

Originality: There are few statistical analysis and machine learning applications for lung cancer prediction in the literature. In this paper, in addition to the prediction model, statistical analyses will be performed, preventive measures will be proposed, and the scope of the analyses will be made holistic by linking it to efficiency in resource utilization. As a result, this will enable professionals to make optimal decisions.