An Efficient Detection of HCC-recurrence in Clinical Data Processing using Boosted Decision Tree Classifier

P. Radha, R. Divya
2020 Procedia Computer Science  
The patient conditions depend on the changes and combination of clinical measures. These clinical measures plays vital role in the detection of HCC recurrence. There are totally, 475 clinical datasets collected, in which 198 hepatocellular carcinoma (HCC) and 277 non hepatocellular carcinoma (non-HCC) were utilized in this investigation study. After the dataset collection, a novelnumerous time series data handling with period joining and statistical measure estimation is planned. Then the
more » ... e ranking and selection is performed, after that multiple measurement boosted decision tree (MMBDT) was utilized as a classification framework to identify HCC reappearance. A multiple(many) measurement of naïve Bayesian (MMNB) was also used as an additional classification model for performance evaluation. Several evaluation measures were utilized to estimate the performance of the projected classification models. A result of the recurrence prediction by MMBDT in 30 days is optimal compared to the prediction performance of the MMNB model. The main motivation and contribution of this research is to use the clinical data mining techniques to identify the HCC occurrences.
doi:10.1016/j.procs.2020.03.196 fatcat:op6dypwsx5dp3nc5rtiyjcaosu