Controllability of Fractional-Order Particle Swarm Optimizer and Its Application in the Classification of Heart Disease

Chou, Fu-I; Huang, Tian-Hsiang; Yang, Po-Yuan; Lin, Chi-Kung; Lin, Tzu-Chao; Ho, Wen-Hsien; Chou, Jyh–Horng

doi:10.3390/app112311517

Cited by 8 publications

(4 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the decision to include or exclude health parameters is analyzed for each individual patient. Particle swarm optimization (PSO) [49] is used to optimize the synaptic weights used in ANNs for better results, which is also computationally inexpensive with respect to the processing and speed of healthcare big data.…”

Section: Cardiac Disease Prediction Using Ann and Psomentioning

confidence: 99%

“…Particle swarm optimization (PSO) provides a global optimized solution based on the population on a d-dimensional space without any prior knowledge of the issues. By taking the advantages of PSO [49], the synaptic weights are optimized in ANN, which result in a time-efficient prediction in the healthcare domain. In PSO, each particle is evaluated by the objective function at its current location.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

Healthcare Big Data Analysis with Artificial Neural Network for Cardiac Disease Prediction

Mohapatra,

Sahoo,

Mohapatra

2023

Electronics

View full text Add to dashboard Cite

The generation of a huge volume of structured, semi-structured and unstructured real-time health monitoring data and its storage in the form of electronic health records (EHRs) need to be processed and analyzed intelligently to provide timely healthcare. A big data analytic platform is an alternative to the traditional warehouse paradigms for the processing, analysis and storage of the tremendous volume of healthcare data. However, the manual analysis of these voluminous, multi-variate patients data is tedious and error-prone. Therefore, an intelligent solution method is highly essential to perform multiple correlation analyses for disease diagnosis and prediction. In this paper, first, a structural framework is proposed to process the huge volume of cardiological big data generated from the hospital and patients. Then, an intelligent analytical model for the cardiological big data analysis is proposed by combining the concept of artificial neural network (ANN) and particle swarm optimization (PSO) to predict the abnormalities in the cardiac health of a person. In the proposed cardiac disease prediction model, an extensive electrocardiogram (ECG) data analysis method is developed to identify the probable normal and abnormal cardiac feature points. Simulation results show the effects of a number of attributes for improving the accuracy of the cardiac disease prediction and data processing time in the cloud with an increase in the number of the cardiac patients.

show abstract

Section: Cardiac Disease Prediction Using Ann and Psomentioning

confidence: 99%

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Healthcare Big Data Analysis with Artificial Neural Network for Cardiac Disease Prediction

Mohapatra,

Sahoo,

Mohapatra

2023

Electronics

View full text Add to dashboard Cite

show abstract

“…We are starting with the work of Chou et al [69], which presents a fractional order particle swarm optimization to improve the classification of heart diseases via an XGBoost classifier. Furthermore [70] presented an improved fractional particle swarm optimization algorithm and shows its applicability to optimize support vector machine and K-Means algorithms.…”

Section: Fractional Gradient-free Optimizationmentioning

confidence: 99%

Combining Fractional Derivatives and Machine Learning: A Review

Raubitzek

Mallinger²,

Neubauer³

2022

Entropy

View full text Add to dashboard Cite

Fractional calculus has gained a lot of attention in the last couple of years. Researchers have discovered that processes in various fields follow fractional dynamics rather than ordinary integer-ordered dynamics, meaning that the corresponding differential equations feature non-integer valued derivatives. There are several arguments for why this is the case, one of which is that fractional derivatives inherit spatiotemporal memory and/or the ability to express complex naturally occurring phenomena. Another popular topic nowadays is machine learning, i.e., learning behavior and patterns from historical data. In our ever-changing world with ever-increasing amounts of data, machine learning is a powerful tool for data analysis, problem-solving, modeling, and prediction. It has provided many further insights and discoveries in various scientific disciplines. As these two modern-day topics hold a lot of potential for combined approaches in terms of describing complex dynamics, this article review combines approaches from fractional derivatives and machine learning from the past, puts them into context, and thus provides a list of possible combined approaches and the corresponding techniques. Note, however, that this article does not deal with neural networks, as there is already extensive literature on neural networks and fractional calculus. We sorted past combined approaches from the literature into three categories, i.e., preprocessing, machine learning and fractional dynamics, and optimization. The contributions of fractional derivatives to machine learning are manifold as they provide powerful preprocessing and feature augmentation techniques, can improve physically informed machine learning, and are capable of improving hyperparameter optimization. Thus, this article serves to motivate researchers dealing with data-based problems, to be specific machine learning practitioners, to adopt new tools, and enhance their existing approaches.

show abstract

“…We're starting with the work of Chou et al [68], which presents a fractional order particle swarm optimization to improve the classification of heart diseases via an XGBoost classifier. Further, [69] presents an improved fractional particle swarm optimization al-gorithm and shows its applicability to optimize support vector machine and K-Means algorithms.…”

Section: Fractional Gradient-free Optimizationmentioning

confidence: 99%

Combining Fractional Derivatives and Supervised Machine Learning: A Review

Raubitzek¹,

Mallinger²,

Neubauer³

2022

Preprint

View full text Add to dashboard Cite

Fractional calculus gained a lot of attention in the last couple of years. Researchers discovered that processes in various fields follow rather fractional dynamics than ordinary integer-ordered dynamics, meaning the corresponding differential equations feature non-integer valued derivatives. There are several arguments for why this is the case, one of them being that fractional derivatives’ inherit spatiotemporal memory and/or the ability to express complex naturally occurring phenomena. Another popular topic nowadays is machine learning, i.e., learning behavior and patterns from historical data. In our ever-changing world with ever-increasing amounts of data, machine learning is a powerful tool for data analysis, problem-solving, modeling, and prediction. It further provides many insights and discoveries in various scientific disciplines. As these two modern-day topics provide a lot of potential for combined approaches to describe complex dynamics, this article reviews combined approaches of fractional derivatives and machine learning from the past, puts them into context, and thus provides a list of possible combined approaches and the corresponding techniques. Note, however, that this article does not deal with neural networks, as there already is profound literature on neural networks and fractional calculus. We sorted past combined approaches from the literature into three categories, i.e., preprocessing, machine learning &amp; fractional dynamics, and optimization. The contributions of fractional derivatives to machine learning are manifold as they provide powerful preprocessing and feature augmentation techniques, can improve physically informed machine learning, and are capable of improving hyperparameter optimization. Thus, this article serves to motivate researchers dealing with data-based problems, to be specific machine learning practitioners, to adopt new tools and enhance their existing approaches.

show abstract

Controllability of Fractional-Order Particle Swarm Optimizer and Its Application in the Classification of Heart Disease

Cited by 8 publications

References 33 publications

Healthcare Big Data Analysis with Artificial Neural Network for Cardiac Disease Prediction

Healthcare Big Data Analysis with Artificial Neural Network for Cardiac Disease Prediction

Combining Fractional Derivatives and Machine Learning: A Review

Combining Fractional Derivatives and Supervised Machine Learning: A Review

Contact Info

Product

Resources

About