Low Complexity Adaptive Nonlinear Models for the Diagnosis of Periodontal Disease

Author(s): Anurag Satpathy*, Ganapati Panda, Rajasekhar Gogula, Renu Sharma

Journal Name: International Journal of Sensors, Wireless Communications and Control

Volume 10 , Issue 4 , 2020

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Graphical Abstract:


Background / Objective: The paper addresses a specific clinical problem of diagnosis of periodontal disease with an objective to develop and evaluate the performance of low complexity Adaptive Nonlinear Models (ANM) using nonlinear expansion schemes and describes the basic structure and development of ANMs in detail.

Methods: Diagnostic data pertaining to periodontal findings of teeth obtained from patients have been used as inputs to train and validate the proposed models.

Result: Results obtained from simulations experiments carried out using various nonlinear expansion schemes have been compared in terms of various performance measures such as Mean Absolute Percentage Error (MAPE), matching efficiency, sensitivity, specificity, false positive rate, false negative rate and diagnostic accuracy.

Conclusion: The ANM with seven trigonometric expansion scheme demonstrates the best performance in terms of all measures yielding a diagnostic accuracy of 99.11% compared to 94.64% provided by adaptive linear model.

Keywords: Periodontal disease, gingivitis, chronic periodontitis, diagnosis, low complexity, adaptive nonlinear model, neural networks, decision support system, soft computing.

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Article Details

Year: 2020
Published on: 18 December, 2020
Page: [508 - 521]
Pages: 14
DOI: 10.2174/2210327909666191211125358
Price: $25

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