Optimized grass hopper algorithm for diagnosis of Parkinson’s disease

Sehgal, Shallu; Agarwal, Manisha; Gupta, Deepak; Sundaram, Shirsh; Bashambu, Arun

doi:10.1007/s42452-020-2826-9

Cited by 18 publications

(11 citation statements)

References 23 publications

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“…The effectiveness of the proposed feature selection method called FSGA is evaluated and compared with other existing approaches, which are; FSJaya (Das et al 2020), MGOA (Sehgal et al 2020), SDS (Shanthi and Rajkumar 2020), and ACO (Sowmiya and Sumitra 2020) by using the considered COVID-19 dataset. These feature selection approaches are described in Table 17.…”

Section: Testing the Proposed Feature Selection Techniquementioning

confidence: 99%

Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy

Mansour¹,

Saleh

Badawy

et al. 2021

J Ambient Intell Human Comput

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The outbreak of Coronavirus has spread between people around the world at a rapid rate so that the number of infected people and deaths is increasing quickly every day. Accordingly, it is a vital process to detect positive cases at an early stage for treatment and controlling the disease from spreading. Several medical tests had been applied for COVID-19 detection in certain injuries, but with limited efficiency. In this study, a new COVID-19 diagnosis strategy called Feature Correlated Naïve Bayes (FCNB) has been introduced. The FCNB consists of four phases, which are; Feature Selection Phase (FSP), Feature Clustering Phase (FCP), Master Feature Weighting Phase (MFWP), and Feature Correlated Naïve Bayes Phase (FCNBP). The FSP selects only the most effective features among the extracted features from laboratory tests for both COVID-19 patients and non-COVID-19 people by using the Genetic Algorithm as a wrapper method. The FCP constructs many clusters of features based on the selected features from FSP by using a novel clustering technique. These clusters of features are called Master Features (MFs) in which each MF contains a set of dependent features. The MFWP assigns a weight value to each MF by using a new weight calculation method. The FCNBP is used to classify patients based on the weighted Naïve Bayes algorithm with many modifications as the correlation between features. The proposed FCNB strategy has been compared to recent competitive techniques. Experimental results have proven the effectiveness of the FCNB strategy in which it outperforms recent competitive techniques because it achieves the maximum (99%) detection accuracy.

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Section: Testing the Proposed Feature Selection Techniquementioning

confidence: 99%

Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy

Mansour¹,

Saleh

Badawy

et al. 2021

J Ambient Intell Human Comput

View full text Add to dashboard Cite

show abstract

“…In this subsection, the IBGA that consists of F score method as a fast method and BGA as an accurate method will be evaluated and compared to many of the modern feature selection methods and also compared to the original dataset without feature selection (Original). The modern selection methods used in the comparison are Genetic Algorithm (GA) [ 2 , 27 , 28 ], Feature Selection via Directional Outliers Correcting (FSDOC) [36] , Orthogonal Least Squares (OLS) based feature selection method [37] , the Modified Grasshopper Optimization Algorithm (MGOA) [38] , and Stochastic Diffusion Search (SDS) algorithm [29] . To evaluate these features selection methods, NB classifier is used as a standard method [ 29 , 30 , 31 ].…”

Section: Resultsmentioning

confidence: 99%

Expecting individuals’ body reaction to Covid-19 based on statistical Naïve Bayes technique

Rabie

Mansour²,

Saleh

et al. 2022

Pattern Recognition

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“…Figure 12 shows the accuracy analysis of the IFSO-DL technique with other recent techniques on the four test datasets [ 27 ]. The figure portrays that the IFSO-DL technique has gained effective outcomes with the maximum accuracy values on all the test datasets.…”

Section: Performance Validationmentioning

confidence: 99%

Metaheuristics with Deep Learning-Enabled Parkinson’s Disease Diagnosis and Classification Model

Bahaddad

Ragab

Ashary

et al. 2022

Journal of Healthcare Engineering

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Parkinson's disease (PD) affects the movement of people, including the differences in writing skill, speech, tremor, and stiffness in muscles. It is significant to detect the PD at the initial stages so that the person can live a peaceful life for a longer time period. The serious levels of PD are highly risky as the patients get progressive stiffness, which results in the inability of standing or walking. Earlier studies have focused on the detection of PD effectively using voice and speech exams and writing exams. In this aspect, this study presents an improved sailfish optimization algorithm with deep learning (ISFO-DL) model for PD diagnosis and classification. The presented ISFO-DL technique uses the ISFO algorithm and DL model to determine PD and thereby enhances the survival rate of the person. The presented ISFO is a metaheuristic algorithm, which is inspired by a group of hunting sailfish to determine the optimum solution to the problem. Primarily, the ISFO algorithm is applied to derive an optimal subset of features with a fitness function of maximum classification accuracy. At the same time, the rat swarm optimizer (RSO) with the bidirectional gated recurrent unit (BiGRU) is employed as a classifier to determine the existence of PD. The performance validation of the IFSO-DL model takes place using a benchmark Parkinson’s dataset, and the results are inspected under several dimensions. The experimental results highlighted the enhanced classification performance of the ISFO-DL technique, and therefore, the proposed model can be employed for the earlier identification of PD.

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Optimized grass hopper algorithm for diagnosis of Parkinson’s disease

Cited by 18 publications

References 23 publications

Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy

Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy

Expecting individuals’ body reaction to Covid-19 based on statistical Naïve Bayes technique

Metaheuristics with Deep Learning-Enabled Parkinson’s Disease Diagnosis and Classification Model

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