Survival Analysis of Hepatocellular Carcinoma
Atmaja Raman1, Harsh Varddhan Singh2, Abhishek Jaiswal3, Rajyashree4
1Atmaja Raman, (Student Btech), Department of Computer Science Engineering, SRM IST, Ramapuram Campus, Chennai (Tamil Nadu), India.
2Harsh Varddhan Singh, (Student Btech), Department of Computer Science Engineering, SRM IST, Ramapuram Campus, Chennai (Tamil Nadu), India.
3Abhishek Jaiswal, (Student B tech), Department of Computer Science Engineering, SRM IST, Ramapuram Campus, Chennai (Tamil Nadu), India.
4Rajyashree, (Faculty), Department of Computer Science Engineering, SRM IST, Ramapuram Campus, Chennai (Tamil Nadu), India.
Manuscript received on 01 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 1774-1778 | Volume-8 Issue-7, May 2019 | Retrieval Number: G5272058719/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper performs the survival analysis for Hepatocellular carcinoma using two different algorithms-Random Forest model and Extreme Gradient Boosting (XGBoost) model. The models were used to perform binary classification. The patients were classified into two classes based on survival time > 10 months and <= 10 months. Results showed that the classification accuracy and misclassification rate of the random forest model was 0.66 and 0.34 respectively. The classification accuracy and misclassification rate of Extreme gradient boost model 0.61 and 0.39 respectively. The Random forest model performed better during testing.
Keyword: Survival Analysis, Hepatocellular Carcinoma, Random Forest, Extreme Gradient Boosting.
Scope of the Article: Predictive Analysis.