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. 2020 Feb 26;6(2):e03402.
doi: 10.1016/j.heliyon.2020.e03402. eCollection 2020 Feb.

A new tool to predict lung cancer based on risk factors

Ahmad S Ahmad  1 , Ali M Mayya  1
Affiliations

A new tool to predict lung cancer based on risk factors

Ahmad S Ahmad et al. Heliyon. .

Abstract

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.

Keywords: Cancer prevention; Computer science; Lung cancer; Lung symptoms; Prediction tool; Risk factors.

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Figures

Figure 1
Figure 1
Analysis of Risk Factors of the Studied Database (colors represent risk levels: blue for high, cyan for medium and red for low), numbers above histograms mean cases from 1000 patients of the database.
Figure 2
Figure 2
Analysis of Symptoms of the Studied Database (colors represent risk levels: blue for high, cyan for medium and red for low).
Figure 3
Figure 3
Statistics of symptoms and factors according to local medical questionnaires (A) For factors (B) for symptoms.
Figure 4
Figure 4
Final risk degree: For factors (A), for symptoms (B).
Figure 5
Figure 5
Random tree generated by RF algorithm according to the analysis of the dataset.
See this image and copyright information in PMC

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