Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis

Abstract

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.

Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics

“…Machine learning algorithms [ 17 , 18 ] such as neural networks for deep learning [20] , polynomial fitting and exponential smoothing are used by many researchers for predicting the spread of COVID-19. In addition, artificial intelligence (AI) has proved its mettle by solving complex healthcare problems like cancer [7] , neurological disorders [19] etc. AI driven models [ 20 , 19 ] can be applied for predicting the growth of infection of the virus and also can help to contain the disease.…”

“…In addition, artificial intelligence (AI) has proved its mettle by solving complex healthcare problems like cancer [7] , neurological disorders [19] etc. AI driven models [ 20 , 19 ] can be applied for predicting the growth of infection of the virus and also can help to contain the disease. But, due to the scarcity of the statistical data neural network has faced overfitting problem.…”

CSCF: a chaotic sine cosine firefly algorithm for practical application problems

“…Then the updated expression is determined in the following section. The fireflies are also referred to as lighting bugs that are found during the night time particularly in the summer season [31]. In chaotic optimization approaches, the chaotic firefly variables replace the random variables [32].…”

FPGA Realizations of Chaotic Epidemic and Disease Models Including Covid-19

“…Many models have been developed for viral infectious diseases such as Influenza and Ebola [9]- [14] and nearly all of them show chaotic dynamics even after vaccination is administrated [15]. The emergence of chaotic behavior can be attributed mostly to predator-prey and competition dynamics [16]- [18] as well as nonlinear interactions between cell populations such as in cancer models [19]- [22] and Parkinson's disease [23], [24].…”