A model to predict the survivability of cancer comorbidity through ensemble learning approach. Issue 3 (5th April 2019)

• Record Type: Journal Article
• Authors: Naghizadeh, Majid; Habibi, Narges
• Other Names: Rocha Álvaro guestEditor.; Anwar Sajid guestEditor.
• Is Part Of: Expert systems. Volume 36:Issue 3(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-04-05
• Subjects: cancer comorbidity -- ensemble learning -- gradient boosting -- SEER database -- survivability rate prediction; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12392
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Cancer is one of the most common death causes worldwide. Breast and genital cancers in women and prostate cancer in men constitute three of the most common cancers. Detection and prevention of these types of cancers are critical objectives. Recent findings indicate that some patients suffer from cancer comorbidity. The probability of survival among patients with comorbid condition is lower than those with only one type of cancer. The importance of concomitant chronic illnesses during cancer treatment through the SEER data is assessed through many machine‐learning approaches. In order to improve the accuracy of prediction of survival rates in patients with cancer and comorbidity of cancers, the gradient boosting ensemble method is adopted for feature selection and modelling. This proposed method increases the accuracy rate and reduces the error rate, and exhibits a significant predictive improvement of survival rates in comorbid cancer compared with the previous proposed models.