Retracted: Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier

Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO) algorithm with the extreme learning machine (ELM) classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN), proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers.

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Sleep stage classification using extreme learning machine and particle swarm optimization for healthcare big data

Journal Of Big Data ◽

10.1186/s40537-020-00406-6 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Nico Surantha ◽

Tri Fennia Lesmana ◽

Sani Muhamad Isa

Keyword(s):

Machine Learning ◽

Big Data ◽

Particle Swarm Optimization ◽

Extreme Learning Machine ◽

Particle Swarm ◽

Sleep Stages ◽

Support Vector ◽

Swarm Optimization ◽

Learning Machine ◽

Portable Sensor

AbstractRecent developments of portable sensor devices, cloud computing, and machine learning algorithms have led to the emergence of big data analytics in healthcare. The condition of the human body, e.g. the ECG signal, can be monitored regularly by means of a portable sensor device. The use of the machine learning algorithm would then provide an overview of a patient’s current health on a regular basis compared to a medical doctor’s diagnosis that can only be made during a hospital visit. This work aimed to develop an accurate model for classifying sleep stages by features of Heart Rate Variability (HRV) extracted from Electrocardiogram (ECG). The sleep stages classification can be utilized to predict the sleep stages proportion. Where sleep stages proportion information can provide an insight of human sleep quality. The integration of Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) was utilized for selecting features and determining the number of hidden nodes. The results were compared to Support Vector Machine (SVM) and ELM methods which are lower than the integration of ELM with PSO. The results of accuracy tests for the combined ELM and PSO were 62.66%, 71.52%, 76.77%, and 82.1% respectively for 6, 4, 3, and 2 classes. To sum up, the classification accuracy can be improved by deploying PSO algorithm for feature selection.

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