Ahmad Ahmad S, Mayya Ali M
Al Andalus University for Medical Science, Faculty of Medical Engineering, Syria.
Heliyon. 2020 Feb 26;6(2):e03402. doi: 10.1016/j.heliyon.2020.e03402. eCollection 2020 Feb.
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.
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.
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.
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.
肺癌是世界上最致命的癌症之一。每年在肺癌治疗、诊断和早期预测领域都会有数百项研究发表。当前的研究重点是通过分析最危险的风险因素来进行肺癌的早期预测。
通过三个阶段设计了一种用于肺癌早期预测的新型工具：分析国际癌症数据库、对当地医疗问卷结果进行分类研究以及从最近发表的医学报告中获取国际医学意见。
使用当地医疗病例对该工具进行测试，并使用当地医学意见来确定所获得分数的准确性。还使用机器学习方法分析来自国际数据集的1000份患者记录，以将我们的结果与国际结果进行比较。
所设计的工具便于为无法进行昂贵医院检查的人群计算风险因素。它不需要输入所有风险信息，并且能在不到一秒的时间内以百分比形式得出肺癌的风险因素。与医学意见的对比研究和性能评估证实了结果的准确性。
