A data mining based clinical decision support system for survival in lung cancer

Abstract-- Lung cancer is leading cause of death in the cancer group. In general, lung cancer has some symptoms, but at an early stage, symptoms are not perceived by the patient. As a result, when patients go to hospital, lung cancer has been diagnosed in middle or high stage. For early detection of lung cancer, necessary a decision support system based on computerized technology that can be utilized by doctor needed to detection lung cancer. The clinical decision support system will help to determine specific medical treatment. The clinical decision support system capable to know data input and produce output result by learning process. The learning process is part of process in artificial neural network (ANN). Many methods used in ANN as Backpropagation (BP)learning algorithm. BP used to produce output result in decision support system. Keywords-- lung cancer, stage, clinical decision support systems, neural network, multilayer perceptron, backpropagation algorithm

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Cloud-Based Automated Clinical Decision Support System for Detection and Diagnosis of Lung Cancer in Chest CT

IEEE Journal of Translational Engineering in Health and Medicine ◽

10.1109/jtehm.2019.2955458 ◽

2019 ◽

pp. 1-1

Author(s):

Anum Masood ◽

Po Yang ◽

Bin Sheng ◽

Huating Li ◽

Ping Li ◽

...

Keyword(s):

Lung Cancer ◽

Decision Support ◽

Decision Support System ◽

Clinical Decision Support ◽

Support System ◽

Clinical Decision Support System ◽

Clinical Decision ◽

Chest Ct ◽

Detection And Diagnosis ◽

Diagnosis Of Lung Cancer

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Concordance, decision impact, and satisfaction for a computerized clinical decision support system in treatment of lung cancer patients

Annals of Oncology ◽

10.1093/annonc/mdz070.010 ◽

2019 ◽

Vol 30 ◽

pp. ii19 ◽

Cited By ~ 1

Author(s):

K.A. Lee ◽

W. Felix ◽

G. Jackson

Keyword(s):

Lung Cancer ◽

Decision Support ◽

Decision Support System ◽

Cancer Patients ◽

Clinical Decision Support ◽

Support System ◽

Clinical Decision Support System ◽

Clinical Decision ◽

Lung Cancer Patients

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A Clinical Decision-Support System Based on Three-Stage Integrated Image Analysis for Diagnosing Lung Disease

Symmetry ◽

10.3390/sym12030386 ◽

2020 ◽

Vol 12 (3) ◽

pp. 386

Author(s):

Ching-Hsue Cheng ◽

Hsien-Hsiu Chen ◽

Tai-Liang Chen

Keyword(s):

Lung Cancer ◽

Image Analysis ◽

Decision Support ◽

Decision Support System ◽

Lung Disease ◽

Clinical Decision Support ◽

Support System ◽

Clinical Decision Support System ◽

Clinical Decision ◽

Ct Image

Thoracic computed tomography (CT) technology has been used for lung cancer screening in high-risk populations, and this technique is highly effective in the identification of early lung cancer. With the rapid development of intelligent image analysis in the field of medical science and technology, many researchers have proposed computer-aided automatic diagnosis methods for facilitating medical experts in detecting lung nodules. This paper proposes an advanced clinical decision-support system for analyzing chest CT images of lung disease. Three advanced methods are utilized in the proposed system: the three-stage automated segmentation method (TSASM), the discrete wavelet packets transform (DWPT) with singular value decomposition (SVD), and the algorithms of the rough set theory, which comprise a classification-based method. Two collected medical CT image datasets were prepared to evaluate the proposed system. The CT image datasets were labeled (nodule, non-nodule, or inflammation) by experienced radiologists from a regional teaching hospital. According to the results, the proposed system outperforms other classification methods (trees, naïve Bayes, multilayer perception, and sequential minimal optimization) in terms of classification accuracy and can be employed as a clinical decision-support system for diagnosing lung disease.

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