Identifying Stroke Indicators Using Rough Sets

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.

Code

• ./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 .m files that are necessary for the computation of the different results in the manuscript.

These .m files use the following user-defined helper scripts.

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 scores
• impactfactor_from_data.m: Computes the impact factor from the data matrix. The script impact_factor.m is 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.