Heart Disease Detection by Using Machine Learning Algorithms and a Real-Time Cardiovascular Health Monitoring System

Nashif, Shadman; Raihan, Md. Rakib; Islam, Md. Rafikul; Imam, Hasan

doi:10.4236/wjet.2018.64057

Cited by 156 publications

(66 citation statements)

References 10 publications

(8 reference statements)

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“…Reference [25] proposed a tentative design of a cloudbased heart disease prediction system using machine learning techniques. Two of the UCI datasets: Cleveland heart disease data consisting of 303 instances with 14 features and VA Long Beach data consisting of 270 instances with also 14 features were merged together making a bigger dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The heart is one of the most essential organs in humans. It is a kind of muscular organ which pumps blood into the body and is the central part of the body's cardiovascular system [25]. The cardiovascular system is composed of all blood vessels such as arteries, veins, and capillaries that form a complex network of blood circulation all over the body [15].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Yahaya¹,

Oye²,

Garba³

2020

AJAI

View full text Add to dashboard Cite

show abstract

“…Nashif et al [11] developed an application to monitor cardiac patients using various machine learning algorithms.The study proposed a cloud-based system that allows patients to upload physiological data to verify cardiac data. Data from the Canadian Community Health Survey data set with 20 important attributes was validated.…”

Section: Related Studymentioning

confidence: 99%

The Prediction of Heart Disease u sing Machine Learning Technique

Choi,

Choi

2020

IJITEE

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Medical errors are generally costly and harmful. There are many deaths worldwide every year. Clinical decision support systems provide opportunities to reduce medical errors and improve patient safety. Certainly, one of the most important aspects of applying such a system is the diagnosis and treatment of heart disease. Machine learning technology is implemented to analyze different kinds of heart-based problems. For this, this study essentially had two primary goals. Firstly, this paper intends to understand the role of variables in heart disease modeling better. Secondly, the study seeks to evaluate the predictive performance of the decision trees. Based on these results, first, men seem to be more susceptible to heart disease than women. Age, chest pain type, resting blood pressure, serum cholesterol, fasting blood sugar, maximum heart rate achieved, exercise-induced angina, ST depression induced by exercise relative to rest, the slope of the peak exercise ST segment, and the number of major vessels also show increased odds of having heart disease. Second, for the full model, the accuracy rate is 0.873, which implies that the error rate is 0.127. Among the patients who predicted not to have heart disease, the accuracy that would not have heart disease was 85.43%, and the accuracy that had heart disease was 89.10% among the patients predicted to have heart disease.

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“…The non invasive based machine learning algorithms are used to diagnose the heart disease [3]. The different constraints of the heart and the body can be used to forecast the heart disease [4].…”

Section: A Literature Reviewmentioning

confidence: 99%

Exploration of Neighbor Kernels and Feature Estimators for Heart Disease Prediction using Machine Learning

Mathew*¹,

Devi²,

Basheer³

2019

IJITEE

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In the growing era of technological world, the people are suffered with various diseases. The common disease faced by the population irrespective of the age is the heart disease. Though the world is blooming in technological aspects, the prediction and the identification of the heart disease still remains a challenging issue. Due to the deficiency of the availability of patient symptoms, the prediction of heart disease is a disputed charge. With this overview, we have used Heart Disease Prediction dataset extorted from UCI Machine Learning Repository for the analysis and comparison of various parameters in the classification algorithms. The parameter analysis of various classification algorithms of heart disease classes are done in five ways. Firstly, the analysis of dataset is done by exploiting the correlation matrix, feature importance analysis, Target distribution of the dataset and Disease probability based on the density distribution of age and sex. Secondly, the dataset is fitted to K-Nearest Neighbor classifier to analyze the performance for the various combinations of neighbors with and without PCA. Thirdly, the dataset is fitted to Support Vector classifier to analyze the performance for the various combinations of kernels with and without PCA. Fourth, the dataset is fitted to Decision Tree classifier to analyze the performance for the various combinations of features with and without PCA. Fifth, the dataset is fitted to Random Forest classifier to analyze the performance for the various levels of estimators with and without PCA. The implementation is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that for KNN classifier, the performance for 12 neighbours is found to be effective with 0.52 before applying PCA and 0.53 after applying PCA. For Support Vector classifier, the rbf kernel is found to be effective with the score of 0.519 with and without PCA. For Decision Tree classifier, before applying PCA, the score is 0.47 for 7 features and after applying PCA, the score is 0.49 for 4 features. For, Random Forest Classifier, before applying PCA, the score is 0.53 for 500 estimators and after applying PCA, the score is 0.52 for 500 estimators.

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Heart Disease Detection by Using Machine Learning Algorithms and a Real-Time Cardiovascular Health Monitoring System

Cited by 156 publications

References 10 publications

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

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

The Prediction of Heart Disease u sing Machine Learning Technique

Exploration of Neighbor Kernels and Feature Estimators for Heart Disease Prediction using Machine Learning

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