Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis
Parkinson’s disease is found as a progressive neurodegenerative condition which affects motor circuit by the loss of up to 70% of dopaminergic neurons. Thus, diagnosing the early stages of incidence is of great importance. In this article, a novel chaos-based stochastic model is proposed by combinin...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719895210 |
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| author | Sujata Dash Ajith Abraham Ashish Kr Luhach Jolanta Mizera-Pietraszko Joel JPC Rodrigues |
| author_facet | Sujata Dash Ajith Abraham Ashish Kr Luhach Jolanta Mizera-Pietraszko Joel JPC Rodrigues |
| author_sort | Sujata Dash |
| collection | DOAJ |
| description | Parkinson’s disease is found as a progressive neurodegenerative condition which affects motor circuit by the loss of up to 70% of dopaminergic neurons. Thus, diagnosing the early stages of incidence is of great importance. In this article, a novel chaos-based stochastic model is proposed by combining the characteristics of chaotic firefly algorithm with Kernel-based Naïve Bayes (KNB) algorithm for diagnosis of Parkinson’s disease at an early stage. The efficiency of the model is tested on a voice measurement dataset that is collected from “UC Irvine Machine Learning Repository.” The dynamics of chaos optimization algorithm will enhance the firefly algorithm by introducing six types of chaotic maps which will increase the diversification and intensification capability of chaos-based firefly algorithm. The objective of chaos-based maps is to select initial values of the population of fireflies and change the value of absorption coefficient so as to increase the diversity of populations and improve the search process to achieve global optima avoiding the local optima. For selecting the most discriminant features from the search space, Naïve Bayesian stochastic algorithm with kernel density estimation as learning algorithm is applied to evaluate the discriminative features from different perspectives, namely, subset size, accuracy, stability, and generalization. The experimental study of the problem established that chaos-based logistic model overshadowed other chaotic models. In addition, four widely used classifiers such as Naïve Bayes classifier, k-nearest neighbor, decision tree, and radial basis function classifier are used to prove the generalization and stability of the logistic chaotic model. As a result, the model identified as the best one and could be used as a decision making tool by clinicians to diagnose Parkinson’s disease patients. |
| format | Article |
| id | doaj-art-3f65a4e6ab9a4fc0a2c02317413670e5 |
| institution | OA Journals |
| issn | 1550-1477 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-3f65a4e6ab9a4fc0a2c02317413670e52025-08-20T02:16:27ZengWileyInternational Journal of Distributed Sensor Networks1550-14772020-01-011610.1177/1550147719895210Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosisSujata Dash0Ajith Abraham1Ashish Kr Luhach2Jolanta Mizera-Pietraszko3Joel JPC Rodrigues4North Orissa University, Baripada, IndiaMachine Intelligence Research (MIR) Labs, Auburn, WA, USAThe Papua New Guinea University of Technology, Lae, Papua New GuineaOpole University, Opole, PolandFederal University of Piauí, Teresina, BrazilParkinson’s disease is found as a progressive neurodegenerative condition which affects motor circuit by the loss of up to 70% of dopaminergic neurons. Thus, diagnosing the early stages of incidence is of great importance. In this article, a novel chaos-based stochastic model is proposed by combining the characteristics of chaotic firefly algorithm with Kernel-based Naïve Bayes (KNB) algorithm for diagnosis of Parkinson’s disease at an early stage. The efficiency of the model is tested on a voice measurement dataset that is collected from “UC Irvine Machine Learning Repository.” The dynamics of chaos optimization algorithm will enhance the firefly algorithm by introducing six types of chaotic maps which will increase the diversification and intensification capability of chaos-based firefly algorithm. The objective of chaos-based maps is to select initial values of the population of fireflies and change the value of absorption coefficient so as to increase the diversity of populations and improve the search process to achieve global optima avoiding the local optima. For selecting the most discriminant features from the search space, Naïve Bayesian stochastic algorithm with kernel density estimation as learning algorithm is applied to evaluate the discriminative features from different perspectives, namely, subset size, accuracy, stability, and generalization. The experimental study of the problem established that chaos-based logistic model overshadowed other chaotic models. In addition, four widely used classifiers such as Naïve Bayes classifier, k-nearest neighbor, decision tree, and radial basis function classifier are used to prove the generalization and stability of the logistic chaotic model. As a result, the model identified as the best one and could be used as a decision making tool by clinicians to diagnose Parkinson’s disease patients.https://doi.org/10.1177/1550147719895210 |
| spellingShingle | Sujata Dash Ajith Abraham Ashish Kr Luhach Jolanta Mizera-Pietraszko Joel JPC Rodrigues Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis International Journal of Distributed Sensor Networks |
| title | Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis |
| title_full | Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis |
| title_fullStr | Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis |
| title_full_unstemmed | Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis |
| title_short | Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis |
| title_sort | hybrid chaotic firefly decision making model for parkinson s disease diagnosis |
| url | https://doi.org/10.1177/1550147719895210 |
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