With the spirit of reproducible research, this repository contains all the codes required to produce the results in the manuscript:
Pathan MS, Jianbiao Z, John D, Nag A, Dev S. Identifying stroke indicators using rough sets. IEEE Access. 2020 Nov 19;8:210318-27.
All codes are written in MATLAB. The manuscript can be accessed from this link.
Please cite the above paper if you intend to use whole/part of the code. This code is only for academic and research purposes.
./Figure3.m: Computes the impact of the dataset size on the correlation value (b/t impact score and accuracy)../Table2_Figure1.m: Computes the performance of the different individual features of electronic health records for detecting stroke../Table3.m: Computes the (our proposed) impact factor scores for the different individual features of electronic health records../Table4_Figure2.m: Computes the benchmarking scores and scatter-plots for the different benchmarking approaches../data/: This folder contains our input data../results/: This folder will save all the results../scripts/: This folder contains helper.mfiles that are necessary for the computation of the different results in the manuscript.
These .m files use the following user-defined helper scripts.
bimodality.m: Computes the bimodality score of a feature vector.find_scores.m: Computes the precision, recall, f-score and accuracy values.impact_factor.m: Computes the impact factor scoresimpactfactor_from_data.m: Computes the impact factor from the data matrix. The scriptimpact_factor.mis a subset of this file.indiscernibility_values_extraction_for_conditional_attributes.m: Computes the indiscernibility values for the conditional attributes.indiscernibility_values_extraction_for_decisional_attribute.m: Computes the indiscernibility values for decisional attribute.l_factors.m: Computes the loading factor scores for the different features from the input data.