Diagnosis and Data Probability Decision Based on Non-Small Cell Lung Cancer in Medical System

There are many factors affecting the survival of people in developing countries, such as the tremendous number of population, nonuniform medical resources, and the threatening of malignant diseases. The improvements in medical information system in developing countries may lead to a bright future. By using effect medical resources and utilizing the information coming from the medical system, the doctors could come to a diagnosis with analysis. The probability of getting sick is very useful information which assists doctors to improve the accuracy of disease diagnosis, shortening treatment time, and reducing the incidence of misdiagnosis. This paper aims to build a model, considering not only probability analysis but also decision making, which can play a crucial role to figure out the probability of non-small lung cancer transitions in four different stages. In each process of the model, selecting effective parameters with big data are adopted for finding maximum effect with the top three high relevancy diagnose and decision data. With effective treatment methods that improve the relevancy diagnose data, the probability of malignant disease development will decrease. It is proved by the statistical analysis of clinical data that the model provides clinical data fast with enough accuracy.