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Comparative Analysis of Medical Diagnostic Techniques Using ANN


Affiliations
1 Department of Computer Science & Technology, Central University of Jammu, Jammu & Kashmir, India
 

An immense and immeasurable amount of data is available to medical experts, extending from points of interest of clinical manifestations to different sorts of biochemical information and yields of imaging gadgets. Each kind of information yields data that must be assessed and relegated to a specific pathology amid the diagnostic process. To rationalize the diagnosis in every day routine and maintain a strategic distance from misdiagnosis, methods of machine learning (particularly ANNs) may be utilized. The versatile learning algorithms of machine learning may deal with several kinds of restorative heterogeneous information and classify them into various class outputs. In this paper, we concisely survey and examine the logic, capacities, and performance of ANNs in medical diagnosis of various diseases by making comparative analysis and focusing more on the medical diagnosis of Diabetes. The use of PID dataset for diagnosis is also demonstrated.

Keywords

Multi-Layer Perceptron Neural Networks (MLPNN), PID (Pima Indian Diabetes Dataset), MLFFN (Multilayer Feedforward Network), BPN (Backpropagation Network), General regression neural network (GRNN), Radial basis function (RBF).
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  • Comparative Analysis of Medical Diagnostic Techniques Using ANN

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Authors

Farooq Ayoub Dar
Department of Computer Science & Technology, Central University of Jammu, Jammu & Kashmir, India
Yashwant Singh
Department of Computer Science & Technology, Central University of Jammu, Jammu & Kashmir, India

Abstract


An immense and immeasurable amount of data is available to medical experts, extending from points of interest of clinical manifestations to different sorts of biochemical information and yields of imaging gadgets. Each kind of information yields data that must be assessed and relegated to a specific pathology amid the diagnostic process. To rationalize the diagnosis in every day routine and maintain a strategic distance from misdiagnosis, methods of machine learning (particularly ANNs) may be utilized. The versatile learning algorithms of machine learning may deal with several kinds of restorative heterogeneous information and classify them into various class outputs. In this paper, we concisely survey and examine the logic, capacities, and performance of ANNs in medical diagnosis of various diseases by making comparative analysis and focusing more on the medical diagnosis of Diabetes. The use of PID dataset for diagnosis is also demonstrated.

Keywords


Multi-Layer Perceptron Neural Networks (MLPNN), PID (Pima Indian Diabetes Dataset), MLFFN (Multilayer Feedforward Network), BPN (Backpropagation Network), General regression neural network (GRNN), Radial basis function (RBF).

References