Effective multiple cancer disease diagnosis frameworks for improved healthcare using machine learning
Effective multiple cancer disease diagnosis frameworks for improved healthcare using machine learning
复制标题
使用机器学习改善医疗保健的有效多种癌症疾病诊断框架
DOI：
10.1016/j.measurement.2021.109145
复制
发表时间：
2021-02-16
期刊：
影响因子：
5.6
通讯作者：
Chung, Yeh-Ching
中科院分区：
文献类型：
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作者：
Hsu, Ching-Hsien;Chen, Xing;Chung, Yeh-Ching
癌症是一种非传染性疾病，随着体内细胞不受控制的生长而进展。癌细胞形成肿瘤，损害免疫系统，导致其他生物变化失灵。最常见的癌症是乳腺癌、前列腺癌、白血病、肺癌和结肠癌。疾病的存在是通过正确的诊断来确定的。许多筛查程序建议在不同阶段发现的条件的存在。医疗从业者进一步分析这些电子健康记录来诊断和治疗个体。在某些情况下，误诊可能是由于手动错误或对数据的误解。为了避免这些问题，本文提出了一种有效的计算机辅助诊断系统支持的智能学习模型。提出了一种基于机器学习的特征建模方法来提高预测性能。从加州大学欧文分校的资料库中获取乳腺癌、宫颈癌和肺癌数据集，以进行本实验研究。监督学习算法被用来训练和验证所提出的系统减少的最佳功能。使用10重交叉验证方法，使用准确度，f分数，精确度和召回率等验证指标评估训练和性能模型。该研究的结果在乳腺癌、宫颈癌和肺癌数据集上分别达到了99.62%、96.88%和98.21%的准确率，这表明了所提出的系统的有效性。此外，该系统作为一种杂项工具，用于从多种类型的癌症疾病的许多临床试验中捕获模式。
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.