Development Of Diagnostic Decision Making For Chronic Hepatitis C Virus Patients By Various Supervised Predictive Model

Ahmed Kareem,Lei Shi,Lin Wei,Yongcai Tao

Hepatitis C virus is a liver disease factors are analyzed that dole out significantly to augment the risk of hepatitis-C virus and blood donors. HCV has an affinity to lead towards chronic infection with time due to its highly mutable nature. The prediction of hepatitis C virus (HCV) is a significant and tedious task in medicine. The healthcare environment is generally perceived as being ‘information rich’ yet ‘knowledge poor’. There is a wealth of data available within the healthcare system. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. Using medical profile such as age, sex, residence and (ALT, AST) enzyme blood tests it can predict the likelihood of patients getting HCV infection. It enables significant knowledge, e.g. patterns, relationships between medical factors related to HCV, to be established. It can serve a training tool to train nurses and medical students to diagnose patients infected with HCV. Using input data from a published study on hepatitis C patients, we demonstrate with various algorithms are easy to apply and produce plausible decision trees. These algorithms confirm common knowledge about the power of laboratory testing to detect liver fibrosis and cirrhosis. Four machine-learning techniques getting to know algorithms namely KNearest Neighbour, Support Vector Machine, Naive Bayes and Decision Tree have been used as prediction, classification, and diagnosis tools are used to hit upon hepatitis C virus. This may be capable of predict the chance degrees of HCV and gives the first-class getting to know set of rules with better accuracy comparatively different algorithms. Machine-learning techniques are used in the medical approaches to help using an invasive method in prediction and detection of diseases, such as prediction of fibrosis, cirrhosis, and prediction of response therapy in Hepatitis C Patient. Then the best algorithm chooses to build the system for the end users using the dataset as database. Then the performance of the algorithms will be tested with appropriate evaluation model, in particular, tenfold cross-validation techniques. Results show that the Decision Tree algorithm gives the better accuracy has its unique strength in realizing the objectives of the defined classification goals.

Volume 12 | Issue 6

Pages: 3113-3123

DOI: 10.5373/JARDCS/V12I6/S20201276