Prediction and Staging of Hepatic Fibrosis in Children with Hepatitis C Virus: A Machine Learning Approach.

OBJECTIVES: The aim of this study is to develop an intelligent diagnostic system utilizing machine learning for data cleansing, then build an intelligent model and obtain new cutoff values for APRI (aspartate aminotransferase-to-platelet ratio) and FIB-4 (fibrosis score) for the prediction and staging of fibrosis in children with chronic hepatitis C (CHC). METHODS: Random forest (RF) was utilized in this study for data cleansing; then, prediction and staging of fibrosis, APRI and FIB-4 scores and their areas under the ROC curve (AUC) have been obtained on the cleaned dataset. A cohort of 166 Egyptian children with CHC was studied. RESULTS: RF, APRI, and FIB-4 achieved high AUCs; where APRI had AUCs of 0.78, 0.816, and 0.77; FIB-4 had AUCs of 0.74, 0.828, and 0.78; and RF had AUCs of 0.903, 0.894, and 0.822, for the prediction of any type of fibrosis, advanced fibrosis, and differentiating between mild and advanced fibrosis, respectively. CONCLUSIONS: Machine learning is a valuable addition to non-invasive methods of liver fibrosis prediction and staging in pediatrics. Furthermore, the obtained cutoff values for APRI and FIB-4 showed good performance and are consistent with some previously obtained cutoff values. There was some agreement between the predictions of RF, APRI and FIB-4 for the prediction and staging of fibrosis.

Intelligible support vector machines for diagnosis of diabetes mellitus.

Diabetes mellitus is a chronic disease and a major public health challenge worldwide. According to the International Diabetes Federation, there are currently 246 million diabetic people worldwide, and this number is expected to rise to 380 million by 2025. Furthermore, 3.8 million deaths are attributable to diabetes complications each year. It has been shown that 80% of type 2 diabetes complications can be prevented or delayed by early identification of people at risk. In this context, several data mining and machine learning methods have been used for the diagnosis, prognosis, and management of diabetes. In this paper, we propose utilizing support vector machines (SVMs) for the diagnosis of diabetes. In particular, we use an additional explanation module, which turns the "black box" model of an SVM into an intelligible representation of the SVM's diagnostic (classification) decision. Results on a real-life diabetes dataset show that intelligible SVMs provide a promising tool for the prediction of diabetes, where a comprehensible ruleset have been generated, with prediction accuracy of 94%, sensitivity of 93%, and specificity of 94%. Furthermore, the extracted rules are medically sound and agree with the outcome of relevant medical studies.