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 Supervised learning, which trains a model on known inputs and output data to predict future outputs  Unsupervised learning, which finds hidden patterns or intrinsic structures in the input data  Semi-supervised learning, which uses a mixture of both techniques; some learning uses supervised data, some learning uses unsupervised learning Machine Learning Unsupervised Learning Supervised learning Develop model based on both input and output data Group and interpret data based only on input data Clustering Classification Regression Predicting cardiovascular disease using electronic health records  681 UK General Practices  383,592 patients free from CVD registered 1 st of January 2005 followed up for years  Two-fold cross validation (similar to other epidemiological studies): n = 295,267 "training set"; n = 82,989 "validation set"  30 separate included features including biometrics, clinical history, lifestyle, test results, prescribing  Four types of models: logistic, random forest, gradient boosting machines, and neural networks

This paper describes an Acute Rheumatic Fever (ARF) Diagnosis Application that is designed and developed based on the proposed Hybrid Approach. It is an integrated framework in terms of a combination of Knowledge-based System, Temporal Theory and Fuzzy Logic. The developed ARF Diagnosis Application was experimentally tested and evaluated by the experts and users of Nepal Heart Foundation (NHF) NHF by means of using NHF's data sets consisting of 676 real patients' records. The ARF Diagnosis Application was found to match 99 percentage of the cases derived from NHF's datasets. The overall ARF diagnostics performance and accuracy was 99.36 percentage.