Title:Low Complexity Adaptive Nonlinear Models for the Diagnosis of Periodontal Disease
VOLUME: 10 ISSUE: 4
Author(s):Anurag Satpathy*, Ganapati Panda, Rajasekhar Gogula and Renu Sharma
Affiliation:Department of Periodontics and Oral Implantology, Institute of Dental Sciences, Siksha ‘O’ Anusandhan University, Khandagiri Square, Bhubaneswar - 751030, Odisha, School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, Argul, Khordha - 752050, Odisha, School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, Argul, Khordha - 752050, Odisha, Department of Electrical Engineering, Institute of Technical Education & Research, Siksha ‘O’ Anusandhan University, Khandagiri Square, Bhubaneswar - 751030, Odisha
Keywords:Periodontal disease, gingivitis, chronic periodontitis, diagnosis, low complexity, adaptive nonlinear model, neural
networks, decision support system, soft computing.
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.
