APPLICATION OF MLP AND RBF ARTIFICIAL NEURAL NETWORKS IN CLINICAL DECISION SUPPORT SYSTEMS TO DIFFERENTIATE BETWEEN COPD AND CHF

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MEHRABI S.; MAGHSOUD LOU M.; ARAB ALI BEYK H.; NOUR MAND R.; NOUZARI YOUNES

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Journal: IRANIAN JOURNAL OF MEDICAL PHYSICS Year:2006 | Volume:3 | Issue:12 Page(s): 49-60

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Title

APPLICATION OF MLP AND RBF ARTIFICIAL NEURAL NETWORKS IN CLINICAL DECISION SUPPORT SYSTEMS TO DIFFERENTIATE BETWEEN COPD AND CHF

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Keywords

CONGESTIVE HEART FAILUREQ2

CHRONIC OBSTRUCTIVE PULMONARY DISEASEQ3

CLINICAL DECISION SUPPORT SYSTEMQ3

MULTILAYER PERCEPTRON NEURAL NETWORKQ3

RADIAL BASIS FUNCTION NEURAL NETWORKQ3

Abstract

Introduction: Decision making and diagnosis of certain diseases can be a challenge for physicians. Neural networks have been used as CLINICAL DECISION SUPPORT SYSTEMs. These networks are powerful algorithms which are capable of learning complex patterns and maintaining their accuracy even in cases when some data is missing. CONGESTIVE HEART FAILURE (CHF) and CHRONIC OBSTRUCTIVE PULMONARY DISEASE (COPD) have many similar symptoms which can make their distinction difficult especially at the time of admission or where the access to echocardiography is limited. Materials and Methods: The multilayer perceptron (MLP) and radial basis function(RBF) neural networks were used to differentiate between CONGESTIVE HEART FAILURE and chronic obstructive pulmonary disorder patients (n=266) using 43 clinical variables which were normalized following consultations with cardiologists. Bayesian regularization was used to improve the generalization of the MLP network. To design the RBF network, K-Means clustering was used to select the centers of radial basis functions, k-nearest neighborhood to define the spread and forward selection to select the optimum number of radial basis functions. A ten-fold cross validation was used to assess the generalization procedure. Results: The MLP led to a sensitivity of 88.3 %, specificity of 83.7% and an area under receiver operating characteristic curve (AUC) of 91.9±1.7 while RBF network resulted in 79.6 % sensitivity, 93 % specificity and AUC of 92.7±1.7. Discussion and Conclusions: Neural networks have been developed as a diagnostic algorithm in many CLINICAL DECISION SUPPORT SYSTEMs and this study like many others confirms their ability to perform well in diagnosing certain diseases such as differentiating between COPD and CHF.

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APA: Copy

MEHRABI, S., MAGHSOUD LOU, M., ARAB ALI BEYK, H., NOUR MAND, R., & NOUZARI, YOUNES. (2006). APPLICATION OF MLP AND RBF ARTIFICIAL NEURAL NETWORKS IN CLINICAL DECISION SUPPORT SYSTEMS TO DIFFERENTIATE BETWEEN COPD AND CHF. IRANIAN JOURNAL OF MEDICAL PHYSICS, 3(12), 49-60. SID. https://sid.ir/paper/96883/en

Vancouver: Copy

MEHRABI S., MAGHSOUD LOU M., ARAB ALI BEYK H., NOUR MAND R., NOUZARI YOUNES. APPLICATION OF MLP AND RBF ARTIFICIAL NEURAL NETWORKS IN CLINICAL DECISION SUPPORT SYSTEMS TO DIFFERENTIATE BETWEEN COPD AND CHF. IRANIAN JOURNAL OF MEDICAL PHYSICS[Internet]. 2006;3(12):49-60. Available from: https://sid.ir/paper/96883/en

IEEE: Copy

S. MEHRABI, M. MAGHSOUD LOU, H. ARAB ALI BEYK, R. NOUR MAND, and YOUNES NOUZARI, “APPLICATION OF MLP AND RBF ARTIFICIAL NEURAL NETWORKS IN CLINICAL DECISION SUPPORT SYSTEMS TO DIFFERENTIATE BETWEEN COPD AND CHF,” IRANIAN JOURNAL OF MEDICAL PHYSICS, vol. 3, no. 12, pp. 49–60, 2006, [Online]. Available: https://sid.ir/paper/96883/en

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