However, at best, the user will still have as many as five algorithms to choose from.
RapidMiner, a data science platform that simplifies machine learning, has tackled this issue by building an app that users can access to reduce the number of algorithms that they may use. Selection is done by narrowing down algorithms to those compatible with the data types present in the data. These algorithms have become so ubiquitous, that the selection process can be extremely extensive. Algorithms are differentiated by their learning style and the form of the prediction function that they produce. Currently, there exists a gamut of machine learning algorithms, which are responsible for producing a prediction function based on the analysis of a known data training set. The most important aspect of machine learning is finding and selecting the right prediction algorithm. Seeking to unlock the potential of machine learning for the general public, companies aim to circumnavigate the skill requirements and package it up in a user-friendly tool, called AutoAI or AutoML. For most people, the costs of conducting machine learning outweigh the benefits that the powerful tool can produce. However, for those not fluent in computer science, the learning curve can prove quite steep and tedious. Alkiviadis VazacopoulosĪs a realization dawns that machines can learn and make inferences from data far more effectively than humans, the application of machine learning has become increasingly commonplace in a variety of fields and methods. Because these processes can be easily adopted in other projects, this environment is attractive for scalable predictive analytics in health research.By Marc Vitenzon and Dr. Using visual tools for ETL on Hadoop and predictive modeling in RapidMiner, robust processes for automatic building, parameter optimization and evaluation of various predictive models, under different feature selection schemes can be developed. Guided by the CRoss-Industry Standard Process for Data Mining (CRISP-DM), the ETL process (Extract, Transform, Load) was initiated by retrieving data from the MIMIC-II tables of interest. As a showcase, a framework was developed for the meaningful use of data from critical care patients by integrating the MIMIC-II database in a data mining environment (RapidMiner) supporting scalable predictive analytics using visual tools (RapidMiner’s Radoop extension). The presentation addresses the problem by focusing on an open, visual environment, suited to be applied by the medical community (RapidMiner). This leaves a gap between potential and actual data usage. Additionally, the application of cutting edge predictive methods and data manipulation require substantial programming skills, limiting its direct exploitation by medical domain experts. However, high-dimensionality and high-complexity of the data involved, prevents data-driven methods from easy translation into clinically relevant models. With the accumulation of large amounts of health related data, predictive analytics could stimulate the transformation of reactive medicine towards Predictive, Preventive and Personalized (PPPM) Medicine, ultimately affecting both cost and quality of. The presentation covers the use of Scalable Predictive Analysis in Critically Ill Patients using a Visual Open Data Analysis Platform (RapidMiner).
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