Prediction system for heart disease using Naive Bayes and particle swarm optimization

Dulhare, Uma N.

doi:10.4066/biomedicalresearch.29-18-620

Cited by 93 publications

(34 citation statements)

References 1 publication

(1 reference statement)

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“…Naive Bayes classifiers are a family of simple probabilistic classifiers by applying bayes theorem with strong independence assumptions between the features [6]. It is the simplest and the fastest probabilistic classifier especially for the training phase [7].…”

Section: Methodsmentioning

confidence: 99%

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A New Cygnus Optimization Algorithm for Prediction Of Cardio Vascular Disease

2019

IJITEE

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Machine learning is an emerging field in the present day due to a massive improvement in the size of data. However, with the current studies, the prologue of artificial intelligence and medical sciences, help in averting any such category of disease. With the intention to take good decision in health care, data mining methodology and technology plays a foremost role to transmit the enormous data into valuable information. Emerging as a primary source of fatality in the early 20th century and peaking in prevalence from 1980s, Cardio-Vascular Disease (CVD) remains a major global threat. Early prediction of CVD is most vital for sensible preclusion and treatment. This paper is to investigate the work on the attribute selection approach and propose an improved Cygnus Optimization Algorithm by carrying a random search through the whole attributes, which was obtained from UCI machine learningrepository. The proposed method shows 97% of accuracy, which is better than early.

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Section: Methodsmentioning

confidence: 99%

“…CVD deals with the heart and allied vascular conditions at immense. According to the American Heart Association (AHA) statistics, 83% of fatality occurs in patients' ≥65 years of age [7]. Cardio-Vascular Disease includes stroke, hypertensive heart disease, cardiomyopathy, congenital heart disease, endocarditic and few more.…”

Section: Introductionmentioning

confidence: 99%

A New Cygnus Optimization Algorithm for Prediction Of Cardio Vascular Disease

2019

IJITEE

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“…Reference [11] proposed a framework that combined the popular Naïve Bayesian classifier and Particle Swarm Optimization (PSO) feature selection algorithm for efficient heart disease prediction. The UCI dataset of VA Long Beach consisting of 270 instances and 14 features was used for the model training and testing processes.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comprehensive Review on Heart Disease Prediction Using Data Mining and Machine Learning Techniques

Yahaya¹,

Oye²,

Garba³

2020

AJAI

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Heart disease is one of the major causes of life complicacies and subsequently leading to death. The heart disease diagnosis and treatment are very complex, especially in the developing countries, due to the rare availability of efficient diagnostic tools and shortage of medical professionals and other resources which affect proper prediction and treatment of patients. Inadequate preventive measures, lack of experienced or unskilled medical professionals in the field are the leading contributing factors. Although, large proportion of heart diseases is preventable but they continue to rise mainly because preventive measures are inadequate. In today's digital world, several clinical decision support systems on heart disease prediction have been developed by different scholars to simplify and ensure efficient diagnosis. This paper investigates the state of the art of various clinical decision support systems for heart disease prediction, proposed by various researchers using data mining and machine learning techniques. Classification algorithms such as the Naïve Bayes (NB), Decision Tree (DT), and Artificial Neural Network (ANN) have been widely employed to predict heart diseases, where various accuracies were obtained. Hence, only a marginal success is achieved in the creation of such predictive models for heart disease patients therefore, there is need for more complex models that incorporate multiple geographically diverse data sources to increase the accuracy of predicting the early onset of the disease.

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“…In 2018, Uma N Dulhare [17]., introduced a model with Naive Bayes classifier and the Particle Swarm Optimization. The results proved that the approach with PSO provided better results in predicting the disease at an early stage.…”

Section: Literature Surveymentioning

confidence: 99%

Heart Disease Prediction System Using Linear Regression, Smoreg, And Rep Trees Algorithms

Nanthini¹,

Pandiselvi²

2019

IJITEE

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The broad area of data mining covers most of the fields of research. Its role in medical diagnosis is very motivative to the researchers. It is very easy for the medical practitioners to analyze and treat the disease of the patients at an early stage. The proposed work deals with predicting heart disease of the patients at an early stage. The method was organized in three stages, Data collection, Data preprocessing and Data classification. The dataset for the work was collected from UCI repository. The collected sample was first preprocessed to clean unwanted information from the dataset. Classification operation is then performed on the preprocessed data. Classification is carried out with three different techniques, Linear regression model, SMOreg and REP trees. The results of the three methods were compared based on Root mean squared error and the Absolute error and are tabulated

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Prediction system for heart disease using Naive Bayes and particle swarm optimization

Cited by 93 publications

References 1 publication

A New Cygnus Optimization Algorithm for Prediction Of Cardio Vascular Disease

A New Cygnus Optimization Algorithm for Prediction Of Cardio Vascular Disease

A Comprehensive Review on Heart Disease Prediction Using Data Mining and Machine Learning Techniques

Heart Disease Prediction System Using Linear Regression, Smoreg, And Rep Trees Algorithms

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