Development of a machine learning-based multimode diagnosis system for lung cancer.

Abstract:

:As an emerging technology, artificial intelligence has been applied to identify various physical disorders. Here, we developed a three-layer diagnosis system for lung cancer, in which three machine learning approaches including decision tree C5.0, artificial neural network (ANN) and support vector machine (SVM) were involved. The area under the curve (AUC) was employed to evaluate their decision powers. In the first layer, the AUCs of C5.0, ANN and SVM were 0.676, 0.736 and 0.640, ANN was better than C5.0 and SVM. In the second layer, ANN was similar with SVM but superior to C5.0 supported by the AUCs of 0.804, 0.889 and 0.825. Much higher AUCs of 0.908, 0.910 and 0.849 were identified in the third layer, where the highest sensitivity of 94.12% was found in C5.0. These data proposed a three-layer diagnosis system for lung cancer: ANN was used as a broad-spectrum screening subsystem basing on 14 epidemiological data and clinical symptoms, which was firstly adopted to screen high-risk groups; then, combining with additional 5 tumor biomarkers, ANN was used as an auxiliary diagnosis subsystem to determine the suspected lung cancer patients; C5.0 was finally employed to confirm lung cancer patients basing on 22 CT nodule-based radiomic features.

journal_name

Aging (Albany NY)

journal_title

Aging

authors

Duan S,Cao H,Liu H,Miao L,Wang J,Zhou X,Wang W,Hu P,Qu L,Wu Y

doi

10.18632/aging.103249

subject

Has Abstract

pub_date

2020-05-23 00:00:00

pages

9840-9854

issue

10

issn

1945-4589

pii

103249

journal_volume

12

pub_type

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