GDL

Proceeding from gdlhub / 2020-12-04 13:23:13
Oleh : Jasmir JASMIR, Siti NURMAINI, Reza Firsandaya MALIK, Dodo Zaenal ABIDIN, Universitas Dinamika Bangsa
Dibuat : 2020-12-04, dengan 1 file

Keyword : text classification, clinical trials, Deep Neural Network, fine grained document clustering
Url : http://www.atlantis-press.com/proceedings/siconian-19/125939962
Sumber pengambilan dokumen : Web

Clinical trials are any research studies involve human participation with health safety outcomes. In clinical trials, there is the most important term called the eligibility criteria (eligible and not eligible). The eligibility criteria for clinical trials are usually written in free text, it requires interpretation from a computer to process them. The purpose of this paper is to classify cancer clinical texts from the public dataset at https://clinicaltrials.gov. The proposed algorithm is Supervised Learning such as K-Nearest Neighbor and Decision Tree, Machine Learning such as Support Vector Machine and Random Forest, Deep Neural Network such as Multilayer Perceptron, and Fine Grained Document Clustering. This research has contributed a new classification model for clinical trial documents and computational value or speed improvement. The results shown the highest accuracy at random forest method 90.5% and the lowest accuracy at multilayer perceptron method that is 72.1%

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