Prediction of heart disease using ensemble learning and Particle Swarm Optimization

Yekkala, Indu; Dixit, Sunanda; Jabbar, M. A.

doi:10.1109/smarttechcon.2017.8358460

Cited by 69 publications

(37 citation statements)

References 8 publications

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“…The idea of ensemble-learning was also applied by aggregating the predictions of different classifiers instead of training an individual classifier. An example of application was conducted for predicting coronary heart disease using bagged tree and AdaBoost algorithms [60]. From this basis, an ensemble method based on neural network has been suggested for creating a more effective classification model and showed promising classification accuracy in [61,62].…”

Section: Related Workmentioning

confidence: 99%

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

et al. 2020

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Heart diseases are highly ranked among the leading causes of mortality in the world. They have various types including vascular, ischemic, and hypertensive heart disease. A large number of medical features are reported for patients in the Electronic Health Records (EHR) that allow physicians to diagnose and monitor heart disease. We collected a dataset from Medica Norte Hospital in Mexico that includes 800 records and 141 indicators such as age, weight, glucose, blood pressure rate, and clinical symptoms. Distribution of the collected records is very unbalanced on the different types of heart disease, where 17% of records have hypertensive heart disease, 16% of records have ischemic heart disease, 7% of records have mixed heart disease, and 8% of records have valvular heart disease. Herein, we propose an ensemble-learning framework of different neural network models, and a method of aggregating random under-sampling. To improve the performance of the classification algorithms, we implement a data preprocessing step with features selection. Experiments were conducted with unidirectional and bidirectional neural network models and results showed that an ensemble classifier with a BiLSTM or BiGRU model with a CNN model had the best classification performance with accuracy and F1-score between 91% and 96% for the different types of heart disease. These results are competitive and promising for heart disease dataset. We showed that ensemble-learning framework based on deep models could overcome the problem of classifying an unbalanced heart disease dataset. Our proposed framework can lead to highly accurate models that are adapted for clinical real data and diagnosis use.

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Section: Related Workmentioning

confidence: 99%

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

et al. 2020

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“…However, the involvement of feature selection in prediction models [14], [16], [17], [19], [22], [25] has not only resulted in the accuracy improvement but also get rid of the problems like greater computational costs and over tting posed by irrelevant input features that involved in the learning process. Apart from these, the techniques may pose designing issues and those can be confronted by the appropriate advanced predictive models in the future research.…”

Section: Discussionmentioning

confidence: 99%

A Comprehensive survey on Heart Disease Prediction using Machine Intelligence

Dogiparthi

Jayanthi

Pillai

2021

Preprint

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Objectives: The latest statistics of World Health Organization anticipated that cardiovascular diseases including Coronary Heart Disease, Heart attack, vascular disease as the biggest pandemic to the world due to which one-third of the world population would die. With the emerging AI trends, applying an optimal machine learning model to target early detection and accurate prediction of heart disease is indispensable to bring down the mortality rates and to treat the cardiac patients with best clinical decision support. This stems for the motivation of this paper. This paper presents a comprehensive survey on heart disease prediction models derived and validated out of popular heart disease datasets like Cleveland dataset, Z-Alizadeh Sani dataset. Methods: This survey was performed using the articles extricated from the Google Scholar, Scopus, Web of Science, Research Gate and PubMed search engines between 2005 to 2020. The main keywords for search were Heart Disease, Prediction, Coronary disease, Healthcare, Heart datasets and Machine Learning.Results: This review explores the shortcomings of various approaches used for the prediction of heart diseases. It outlines pros and cons of different research methodologies along with the validation parameters of each reviewed publication.Conclusion: The machine intelligence can serve as a genuine alternative diagnostic method for prediction, which will in turn keep the patients well aware of their illness state. Despite the researcher’s efforts, still uncertainty exist towards standardization of prediction models which demands further exploration of optimal prediction models.

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“…In order to reliably envisage the existence of heart illness for a specific sufferer, this paper [1] analyzes different ensemble approaches Bagged Tree, Random Forest, and AdaBoost with the variable sub categorization process -Particle Swarm Optimization (PSO). Experimental findings indicate that the highest precision was obtained by Bagged Tree and PSO.…”

Section: Literature Surveymentioning

confidence: 99%

Applying Ensemble Learning Algorithm in Early Prognosis of Heart Illness

Jaffrin¹

2021

TURCOMAT

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Medical diagnosis and treatment of diseases are the key elements of machine learning algorithms nowadays. To find similarities between various diseases, machine learning algorithms are used. Many people are now dying due to sudden heart attacks. Predicting and diagnosing heart disease is a daunting aspect faced by physicians and hospitals around the world. There is a need to foreknow whether or not a person is at risk of heart syndrome in advance, in order to minimize the number of deaths due to heart disease. In this field, machine learning algorithms play a very significant role. Many researchers are carrying out their research in this field to create software that can assist doctors to make decisions about cardiac illness prognosis. In this paper, Random Forest and AdaBoost ensemble Machine Learning Procedures are used in advance to predict heart disease. The datasets are handled in python programming by means of Anaconda Spyder IDE to validate the machine learning algorithm.

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Prediction of heart disease using ensemble learning and Particle Swarm Optimization

Cited by 69 publications

References 8 publications

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

A Comprehensive survey on Heart Disease Prediction using Machine Intelligence

Applying Ensemble Learning Algorithm in Early Prognosis of Heart Illness

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