Parkinson Disease Classification based on binary coded genetic algorithm and Extreme learning machine

Abstract

In this paper, we propose a Binary Coded Genetic Algorithm combined with Extreme learning machine (BCGA-ELM) for Parkinson Disease classification problem. Proposed method analyses ParkDB data base of 22283 genes' expression information extracted from 22 normal patients and 50 Parkinson Disease patients. Proposed method can sufficiently recognize PD patients among normal persons using gene expression information. Besides, the proposed method can also find subset of genes which may be responsible for Parkinson Disease. Chosen subset of genes causes the maximum generalization performance for PD classification problem. Proposed BCGA-ELM also produces a robust solution. In our experiments we executed BCGA-ELM twice started from randomly generated initial data and found same solution at the end. Detected set of 19 genes was also verified by SVM and PBL-McRBFN. Both methods caused maximum generalization performance.

A Comprehensive Review on AI-Enabled Models for Parkinson’s Disease Diagnosis

“…Indeed, 75.4% of the 195 samples tested positive for PD, with the rest being healthy. Sachdev and Kim [78] studied the gait characteristics of 93 PD patients and 73 healthy adults. The disease has been discovered to utilize multiple biomarkers, which have been used in various investigations to identify the onset of the condition and its associated issues.…”

“…The results of the experiments clearly showed that the suggested technique could provide a similar solution for the PD classification issue for various random initializations. They intend to conduct a medical investigation of the 19 genes they chose in their upcoming research [78].…”

Extreme Learning Machines on High Dimensional and Large Data Applications: A Survey

“…Benefiting from the easy implementation for real-time diagnosing and the relatively convincing performance, adopting ELM for medical signal processing has attracted increasing attention from the research community in the past several years. To the best of our capability, we have collected dozens of articles discussing ELM on various medical applications in this paper, including cardiac arrhythmia classification [69], gene cancer identification [70,71], mammographic microcalcifications detection [72], epileptic diagnosis [73][74][75], liver parenchyma and tumor detection [76][77][78], EEG vigilance [79], magnetic resonance images (MRI) data processing [80], gene selection [81], protein sequence applications [82][83][84][85], hypoglycemia prediction [86], and Parkinson classification [87].…”

“…The prediction accuracy on three prediction horizons with different time durations is compared between the basic ELM and the regularized ELM. Sachnev and Kim [87] studied the Parkinson disease (PD) classification using ELM. The benchmark ParkPD database consisting of more than 22 thousand genes' expressions collected from normal and PD patients is tested with the classifier.…”

Efficient classification of Parkinson's disease using extreme learning machine and hybrid particle swarm optimization

“…Genetic Algorithm (GA)-based feature selection of PD dataset was proposed in [18] where the classification task of PD patients based on the extracted feature vector was carried out using SVM. In [19], a binary coded GA algorithm was designed to select the best features of gene expression ParkDB dataset in which the selected feature set is employed in Extreme Learning Machine (ELM) approach for PD classification. One of the main problems of the mentioned methods is processing the entire of feature set to classify the PD disease in which the classification results may be downfall due to the presence of irrelevant features in the dataset.…”