![]() ![]() WEKA comes with multiple sample datasets, so the user can train on validated data. Data can also be uploaded from a URL or a remote database. arff file (attribute-relation file format), which is a text file that includes metadata. Not only is the software widely used, but it is also complemented by a textbook, an online course, and extensive YouTube instructional videos. The exception to this list of visual operator-based machine learning applications is WEKA that was developed at the University of Waikato in New Zealand. For example, ML learners should understand concepts such as bias, variance, and underfitting/overfitting. A variety of low cost online courses are available to supplement knowledge in machine learning. Ideally, users should have some familiarity with biostatistics as a prerequisite and continued reading is important to become more knowledgeable. These types of programs offer a good introduction to machine learning, but they are infrequently cited as the analytical method used in published studies.Īdopting one or more of these programs will provide an excellent introduction to machine learning but will not make the user an expert. Unfortunately, the visual programming process is similar, but not identical for the three programs. While visual programming is appropriate for visual learners, it does require learning the proper steps and sequences in order to get results. There is a learning curve, but it is relatively short. This is referred to as "visual programming" but it is not all like learning to write scripts or code in R or Python. Each program presented will have a screenshot to make this clearer. The rest of the platforms require dragging a widget (Orange) or operator (RapidMiner) or node (KNIME) onto a field/window and connecting the icons together, in order to perform a function or process. The first ML platform WEKA requires no programming or math skills and the graphical user interface (GUI) is intuitive, in terms of uploading data and navigating the software. The following is a list of several well-known free ML choices: WEKA, Orange, RapidMiner, and KNIME. All machine learning programs offer algorithms for supervised and unsupervised learning, but some also offer text and image mining. For example, random forests are useful when dealing with missing data or building a non-linear model. Algorithms can be fine-tuned, and all have strengths and weaknesses. It is worth noting that several algorithms, such as decision trees, SVM and neural networks can be used for both classification and regression. ![]() They found as an example, if a patient was prescribed calcium, they also received vitamin D using the association rule algorithm "Apriori." ![]() A study looked for possible associations in a large drug dataset. Association rules look for associations between variables and the best example is market basket analysis, where shoppers who buy e.g. For example, in one study a cluster analysis was applied to a dataset of 161 children with severe asthma and four separate new phenotypes were identified. Cluster analysis looks for hidden patterns in data and has been extensively used in genomics to uncover genotypic and phenotypic patterns. Unsupervised learning means you don't know the outcome of analysis beforehand and it is further broken down into cluster analysis and association rules. Regression is used for scenarios where the outcome (or dependent variable) is continuous (numerical) data, such as healthcare cost in dollars. Classification is used for predictions when the outcome (class) is categorical or nominal data, such as the patient lived or died or heart disease or no heart disease (binary or two choices). Supervised learning is further broken down into classification and regression. Supervised learning indicates that you know the desired outcome of interest. Machine learning (ML) is classified into supervised and unsupervised learning. ![]() The purpose of this article is to discuss several free open source programs that should be of interest to anyone trying to learn more about machine learning, without the need to know a programming language or higher math. Academic medical centers and universities commonly have licenses for commercial statistical/machine learning packages so this may be their best choice. Users can choose from commercial expensive applications such as Microsoft Azure Machine Learning Studio, SAS Artificial Intelligence Solutions or IBM SPSS Modeler. Parts 1 and 3 can be read here and here.Ī variety of machine learning tools are now available that can be part of the armamentarium of many industries, to include healthcare. Bob HoytThis is the second article in a series of articles on the use of machine learning in healthcare by Bob Hoyt MD FACP. ![]()
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