000			02777nam a22001457a 4500
008			210809b ||||| |||| 00| 0 eng d
100			_aOWIE-OBAZEE, Esosa Deris _98713
245			_aDevelopment of a Predictive Model for the Classification of the Survival of Hepatitis Patients Using Decision Trees Algorithm
250			_aProf. IDOWU, P. A.
260			_aIbafo _bComputer Science and Mathematics _c2020
300			_axi,; 74p. _bdia, tables
520			_aThis study is aimed at the application of decision trees algorithm for the classification of the survival of patients living with hepatitis C virus. The study achieved this by eliciting knowledge on the variables monitored during the treatment of Hepatitis C patients and collecting relevant data, formulating the classification model, simulating the model and validating the model. Structured interview was used to elicit information on the variables monitored among Hepatitis patients receiving treatment. C4.5 decision trees algorithm was used to formulate the classification model for the survival of Hepatitis C disease based on the information collected. The classification model was simulated using Waikato Environment for Knowledge Analysis (WEKA) software. The classification model was validated based on accuracy, sensitivity, false alarm rate and precision. The results of the study showed that 19 variables were associated with the classification of the survival of patients receiving treatment for Hepatitis C. The results also identified a number of variables which had missing values from the data collected. The results of the simulation showed that the higher the proportion of datasets use for training then the higher the performance of the model on the testing dataset. Therefore, using 90% of the dataset for training and 10% of the dataset for testing showed the highest performance. The results also showed that twelve rules were inferred from the decision trees which was formulated from the simulation with the best performance. The results also showed that among the 19 variables identified, 11 variables were the most relevant, namely: ascites, age, spiders, bilirubin, liver firmness, sgot, palpable spleen, anorexia, and liver size. The study concluded that the classification model developed can be adopted for use by physicians in locations where there are no experts to provide as decision support vi thus improving survival which may mitigate untimely deaths. The study also concluded that the identification of a smaller yet relevant number of variables will reduce the challenge of focusing on irrelevant variables monitored thus reducing the response time of data collection and survival classification.
650			_aComputer Science _91105
942			_cTHS
999			_c6176 _d6176