Figure 4.

Suggested an efficient platform for medical databases with big data (large number of inputs (features)), which include clinical, demographic, socioeconomic and genetic factors. The platform starts with feeding a data set imported from the corresponding medical database. Genetic algorithm loop randomly selects different subsets (chromosomes) of feature to use them in the prediction process, it starts with “feature selection start” and ends with “feature selection end”, and this algorithm saves time in analyzing large databases. Partitioning divides data into training and testing sets, and the scorer evaluates the prediction accuracy for the testing set, the results for different chromosomes (subsets of features) can be exported and saved as output file (i.e., excel sheet). Different learners and predictors can be selected and evaluated for their performance in prediction. This platform can be embedded in the database for automatic analysis or called independently.