Effective multiple cancer disease diagnosis frameworks for improved healthcare using machine learning. (April 2021)

• Record Type: Journal Article
• Authors: Hsu, Ching-Hsien; Chen, Xing; Lin, Weiwei; Jiang, Chuntao; Zhang, Youhong; Hao, Zhifeng; Chung, Yeh-Ching
• Is Part Of: Measurement. Volume 175(2021)
• Publication Date: 2021-04
• Subjects: Cancer -- Computer-aided diagnosis -- Computer modeling -- Clinical trials -- Machine learning; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.109145
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Proposed Machine learning-based feature modeling improve predictive performance. Study attained 99.62, 96.88 and 98.21% accuracy on breast, cervical & lung cancer. Screening procedures are suggested to find presence of different stages condition. System acts as miscellaneous tool for capturing pattern from clinical trials. Abstract: Cancer is a kind of non-communicable disease, progresses with uncontrolled cell growth in the body. The cancerous cell forms a tumor that impairs the immune system, causes other biological changes to malfunction. The most common kinds of cancer are breast, prostate, leukemia, lung, and colon cancer. The presence of the disease is identified with the proper diagnosis. Many screening procedures are suggested to find the presence of the condition under different stages. Medical practitioners further analyze these electronic health records to diagnose and treat the individual. In some cases, misdiagnosis can happen due to manual error or misinterpretation of the data. To avoid these issues, this paper presents an effective computer-aided diagnosis system supported by intelligence learning models. A machine learning-based feature modeling is proposed to improve predictive performance. From the University of California, Irvine repository, breast, cervical, and lung cancer datasets are accessed to conduct this experimental study. Supervised learning algorithms are employed to train and validate the optimal features reduced by the proposed system. Using the 10-Fold cross-validation method, the trained and performance model is evaluated with validation metrics such as accuracy, f-score, precision, and recall. The study's outcome attained 99.62%, 96.88%, and 98.21% accuracy on breast, cervical, and lung cancer datasets, respectively, which exhibits the proposed system's efficacy. Moreover, this system acts as a miscellaneous tool for capturing the pattern from many clinical trials for multiple types of cancer disease.