An Intelligent Learning System Based on Random Search Algorithm and Optimized Random Forest Model for Improved Heart Disease Detection

Javeed, Ashir; Zhou, Shijie; Liao, Yongjian; Qasim, Iqbal; Noor, Adeeb; Nour, Redhwan

doi:10.1109/access.2019.2952107

Cited by 208 publications

(83 citation statements)

References 33 publications

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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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Section: Discussionmentioning

confidence: 99%

A Comprehensive survey on Heart Disease Prediction using Machine Intelligence

Dogiparthi

Jayanthi

Pillai

2021

Preprint

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“…Because of this utilizing this proposed model, they have 93.33% characterization precision utilizing DNN. Compared with traditional ANN framework this model gives 3.33% more [13].…”

Section: Literature Surveymentioning

confidence: 96%

A Survey on Cardiovascular Prediction using Variant Machine learning

Chandrika¹,

Madhavi²,

Sindhuja³

et al. 2021

E3S Web Conf.

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Prediction of a cardiovascular diseases has always a tedious challenge for doctors and medical practitioners. Most of the practitioners and hospital staff offers expensive medication, care and surgeries to treat the cardiovascular patients. At early-stage of prediction of heart-oriented problems will be giving a chance of survival by taking necessary precautions. Over the years there are different types of methodologies were proposed to predict the cardiovascular diseases one of the best methodologies is a Machine learning approach. These years many scientific advancements take place in the Artificial Intelligence, Machine learning, and Deep learning which gives an extra push up to help and implement the path in the field of medical image processing and medical data analysis. By using the enormous dataset from various medical experts used to help the researchers to predict the coronary problems prior to happening. Many researchers have tried and implemented different machine learning algorithms to automate the prediction analysis using the enormous number of datasets. There are numerous algorithms and procedures to predict the cardiovascular diseases and accessible to be specific Classification methods including Artificial Neural Networks (AI), Decision tree (DT), Support vector machine (SVM), Genetic algorithm (GA), Neural network (NN), Naive Bayes (NB) and Clustering algorithms like K-NN. A few examinations have been done for creating expectation models utilizing singular procedures and additionally concatenating at least two strategies. This paper gives a speedy and simple survey and knowledge of approachable prediction models using different researchers work from 2004 to 2019. The examination indicates the precision of individual experiments done by various researchers.

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“…(3) Random Forest (RF) RF algorithm is to generate a number of different data sets by sampling, and then train a classification tree on each data set. Each tree will participate in the final decision of prediction results [22]. The advantage of RF algorithm is that it has good robustness when dealing with missing data and strong reliability when dealing with tasks with more variables [23].…”

Section: (1) Support Vector Machine (Svm)mentioning

confidence: 99%

A Novel Multi-Branch Channel Expansion Network for Garbage Image Classification

2020

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Due to the lack of data available for training, deep learning hardly performed well in the field of garbage image classification. We choose the TrashNet data set which is widely used in the field of garbage image classification, and try to overcome data deficiencies in this field by optimizing the network structure. In this paper, it is found that the deeper network and short-circuit connection, which are generally accepted in the field of deep learning, will not work well on the TrashNet data set. By analyzing and modifying the network structure, we propose an effective method to improve the network performance on TrashNet data set. This method widens the network by expanding branches, and then uses add layers to realize the fusion of feature information. It can make full use of feature information at slight additional computational cost. Using this method to replace the core structure of the Xception network, the performance of the improved network has been improved greatly. Finally, the M-b Xception network proposed by us achieves 94.34% classification accuracy on the TrashNet data set, and has certain advantages over some state-of-theart methods on multiple indicators. The python code can be download from https://github.com/scp19801980/Trash-classify-M_b-Xception.

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An Intelligent Learning System Based on Random Search Algorithm and Optimized Random Forest Model for Improved Heart Disease Detection

Cited by 208 publications

References 33 publications

A Comprehensive survey on Heart Disease Prediction using Machine Intelligence

A Comprehensive survey on Heart Disease Prediction using Machine Intelligence

A Survey on Cardiovascular Prediction using Variant Machine learning

A Novel Multi-Branch Channel Expansion Network for Garbage Image Classification

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