HLNBO: Hybrid Leader Namib Beetle Optimization Algorithm-Based LeNet for Classification of Parkinson’s Disease

Abstract

Parkinson’s disease (PD) occurs while particular cells of the brain are not able to create dopamine that is required for regulating the count of non-motor as well as motor activities of the human body. One of the earlier symptoms of PD is voice disorder and current research shows that approximately about 90% of patients affected by PD suffer from vocal disorders. Hence, it is vital to extract pathology information in voice signals for detecting PD, which motivates to devise the approaches for feature selection and classification of PD. Here, an effectual technique is devised for the classification of PD, which is termed as Hybrid Leader Namib beetle optimization algorithm-based LeNet (HLNBO-based LeNet). The considered input voice signal is subjected to pre-processing of the signal phase. The pre-processing is carried out to remove the noises and calamities using a Gaussian filter whereas in the feature extraction phase, several features are extracted. The extracted features are given to the feature selection stage that is performed employing the Hybrid Leader Squirrel Search Water algorithm (HLSSWA), which is the combination of Hybrid Leader-Based Optimization (HLBO), Squirrel Search Algorithm (SSA), and Water Cycle Algorithm (WCA) by considering the Canberra distance as the fitness function. The PD classification is conducted using LeNet, which is tuned by the designed HLNBO. Additionally, HLNBO is newly presented by merging HLBO and the Namib beetle optimization algorithm (NBO). Thus, the new technique achieved maximal values of accuracy, TPR, and TNR of about 0.949, 0.957, and 0.936, respectively.