J Appl Biomed 17:75, 2019 | DOI: 10.32725/jab.2018.007

Deep stacked sparse auto-encoders for prediction of post-operative survival expectancy in thoracic lung cancer surgery

Mohammad Saber Iraji *
Payame Noor University, Faculty of Engineering, Department of Computer Engineering and Information Technology, Tehran, Iran

Lung cancer is the leading cause of cancer death in men and women. The prognostic value of survival after lung cancer surgery has an important role in decision-making for surgeons and patients. The combination of clinical features and CT scan information for diagnosis, treatment and survival of patients with lung cancer increases the accuracy of prediction using machine learning. Therefore, creating a computer intelligent method with low error and high accuracy to predict survival is an important challenge, and it is beneficial for decreasing mortality from lung cancer, and for planning treatment. In this work, we implemented a deep stacked sparse auto-encoder (DSSAE) approach on a thoracic surgery data set for 470 patients, and our results contributing to deep learning based on 16 features were more precise than other suggested techniques for predicting post-operative survival expectancy in thoracic lung cancer surgery. The proposed method achieved a sensitivity of 94%, specificity of 82.86% and g-mean of 88.25%.

Keywords: Deep stacked sparse auto-encoders (DSSAEs); Lung cancer; Neural networks; Thoracic surgery
Conflicts of interest:

The authors have no conflict of interests to declare.

Received: November 6, 2018; Accepted: December 3, 2018; Prepublished online: January 10, 2019; Published: March 19, 2019  Show citation

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Saber Iraji M. Deep stacked sparse auto-encoders for prediction of post-operative survival expectancy in thoracic lung cancer surgery. J Appl Biomed. 2019;17(1):75. doi: 10.32725/jab.2018.007. PubMed PMID: 34907749.
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