Background : Lung cancer is one of the deadliest cancer in the world. Hundreds of researches are presented annually in the field of lung cancer treatment, diagnosis and early prediction. The current research focuses on the early prediction of lung cancer via analysis of the most dangerous risk factors.
Methods : A novel tool for the early prediction of lung cancer is designed following three stages: the analysis of an international cancer database, the classification study of the results of local medical questionnaires and the international medical opinion obtained from recently published medical reports.
Results : The tool is tested using local medical cases and the local medical opinion(s) is (are) used to determine the accuracy of the scores obtained. The Machine Learning approaches are also used to analyze 1000 patient records from an international dataset to compare our results with the international ones.
Conclusions : The designed tool facilitates computing the risk factors for people who are unable to perform costly hospital tests. It does not require entering all risk inputs and produces the risk factor of lung cancer as a percentage in less than a second. The comparative study with medical opinion and the performance evaluation have confirmed the accuracy of the results.
Ahmad Ahmad S, Mayya Ali M
Cancer prevention, Computer science, Lung cancer, Lung symptoms, Prediction tool, Risk factors