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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

Developing a Decision Support System (DSS) for Rheumatic Fever (RF) is complex due to the levels of vagueness, complexity and uncertainty management involved, especially when the same arthritis symptoms can indicate multiple diseases. It is this inability to describe observed symptoms precisely that necessitates our approach to developing a Decision Support System (DSS) for diagnosing arthritis pain for RF patients using fuzzy logic. In this paper we describe how fuzzy logic could be applied to the development of a DSS application that could be used for diagnosing arthritis pain (arthritis pain for rheumatic fever patients only) in four different stages, namely: Fairly Mild, Mild, Moderate and Severe. Our approach employs a knowledge-base that was built using WHO guidelines for diagnosing RF, specialist guidelines from Nepal and a Matlab fuzzy tool box as components to the system development. Mixed membership functions (Triangular and Trapezoidal) are applied for fuzzification and Mamdani-type is used for the fuzzy reasoning process. Input and output parameters are defined based on the fuzzy set rules.

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