Framework for the Development of Data-Driven Mamdani-Type Fuzzy Clinical Decision Support Systems.

Yamid Fabián Hernández-Julio,Alexander Guerrero-Avendaño,Wilson Nieto-Bernal,Martha Janeth Prieto-Guevara,Inés Meriño-Fuentes

doi:10.3390/diagnostics9020052

Abstract

Clinical decision support systems (CDSS) have been designed, implemented, and validated to help clinicians and practitioners for decision-making about diagnosing some diseases. Within the CDSSs, we can find Fuzzy inference systems. For the reasons above, the objective of this study was to design, to implement, and to validate a methodology for developing data-driven Mamdani-type fuzzy clinical decision support systems using clusters and pivot tables. For validating the proposed methodology, we applied our algorithms on five public datasets including Wisconsin, Coimbra breast cancer, wart treatment (Immunotherapy and cryotherapy), and caesarian section, and compared them with other related works (Literature). The results show that the Kappa Statistics and accuracies were close to 1.0% and 100%, respectively for each output variable, which shows better accuracy than some literature results. The proposed framework could be considered as a deep learning technique because it is composed of various processing layers to learn representations of data with multiple levels of abstraction.

Highlights

Clinical decision support systems (CDSS) have been designed, implemented, and validated to help clinicians and practitioners with decision-making about diagnosing some diseases by managing data or medical knowledge [1]
For the validation of the performance of the data-driven Mamdani-type fuzzy Clinical decision support systems using clusters and pivot table, we compared our results with other models obtained from the literature (Section 4.3)
The results shown in all mentioned tables (Tables 9–13) indicate that the proposed framework for the development of data-driven Mamdani-type fuzzy clinical decision support systems can be used as a reliable approach for helping in the decision-making processes

Summary

Introduction

Clinical decision support systems (CDSS) have been designed, implemented, and validated to help clinicians and practitioners with decision-making about diagnosing some diseases by managing data or medical knowledge [1]. Within these CDS systems, we can find several techniques: Machine Learning (ML) [2], Deep Learning (DL) [3], and Fuzzy logic (FL) systems [4,5]. According to Reference [2], among the first techniques, we can find k-Nearest Neighbor—k-NN, Artificial Neural Network—ANN, Decision Tree—DT, Support Vector Machine—SVM, random forest, among others. DSS, by default, comprise interactive features to aid enough data and model analysis with the intent to classify and resolve predicaments as well as present resolutions [13]

Objectives

Methods

Results

Discussion

Conclusion