Heart disease prediction using machine learning techniques : a survey

Ramalingam, V.; Dandapath, Ayantan; Raja, Mahalakshmi

doi:10.14419/ijet.v7i2.8.10557

Cited by 246 publications

(73 citation statements)

References 14 publications

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“…Ramalingam et al, used several machine learning algorithms such as support vector machine and naïve Bayes, to name but a few. It shows that the naïve Bayes model was computationally very fast and both the SVM and naïve Bayes performed extremely well [16].…”

Section: Discussionmentioning

confidence: 98%

Comparison of Support Vector Machine, Naïve Bayes and Logistic Regression for Assessing the Necessity for Coronary Angiography

Golpour

Ghayour‐Mobarhan

Saki

et al. 2020

IJERPH

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(1) Background: Coronary angiography is considered to be the most reliable method for the diagnosis of cardiovascular disease. However, angiography is an invasive procedure that carries a risk of complications; hence, it would be preferable for an appropriate method to be applied to determine the necessity for angiography. The objective of this study was to compare support vector machine, naïve Bayes and logistic regressions to determine the diagnostic factors that can predict the need for coronary angiography. These models are machine learning algorithms. Machine learning is considered to be a branch of artificial intelligence. Its aims are to design and develop algorithms that allow computers to improve their performance on data analysis and decision making. The process involves the analysis of past experiences to find practical and helpful regularities and patterns, which may also be overlooked by a human. (2) Materials and Methods: This cross-sectional study was performed on 1187 candidates for angiography referred to Ghaem Hospital, Mashhad, Iran from 2011 to 2012. A logistic regression, naive Bayes and support vector machine were applied to determine whether they could predict the results of angiography. Afterwards, the sensitivity, specificity, positive and negative predictive values, AUC (area under the curve) and accuracy of all three models were computed in order to compare them. All analyses were performed using R 3.4.3 software (R Core Team; Auckland, New Zealand) with the help of other software packages including receiver operating characteristic (ROC), caret, e1071 and rminer. (3) Results: The area under the curve for logistic regression, naïve Bayes and support vector machine were similar—0.76, 0.74 and 0.75, respectively. Thus, in terms of the model parsimony and simplicity of application, the naïve Bayes model with three variables had the best performance in comparison with the logistic regression model with seven variables and support vector machine with six variables. (4) Conclusions: Gender, age and fasting blood glucose (FBG) were found to be the most important factors to predict the result of coronary angiography. The naïve Bayes model performed well using these three variables alone, and they are considered important variables for the other two models as well. According to an acceptable prediction of the models, they can be used as pragmatic, cost-effective and valuable methods that support physicians in decision making.

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Section: Discussionmentioning

confidence: 98%

Comparison of Support Vector Machine, Naïve Bayes and Logistic Regression for Assessing the Necessity for Coronary Angiography

Golpour

Ghayour‐Mobarhan

Saki

et al. 2020

IJERPH

View full text Add to dashboard Cite

show abstract

“…As per the researches discussed above, still different kind of models are providing variation in the prediction score. Thus, the dimensionality reduction and feature engineering can improve the process of data selection, which ultimately can improve the accuracy estimation [21]. In conclusion, the clear research gap found in the previous researches is that, the measured accuracy is not up to the mark.…”

Section: Machine Learning Classifiers For Heart Disease Predictionmentioning

confidence: 91%

Implementation of Machine Learning Model to Predict Heart Failure Disease

Alotaibi¹

2019

IJACSA

190

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In the current era, Heart Failure (HF) is one of the common diseases that can lead to dangerous situation. Every year almost 26 million of patients are affecting with this kind of disease. From the heart consultant and surgeon's point of view, it is complex to predict the heart failure on right time. Fortunately, classification and predicting models are there, which can aid the medical field and can illustrates how to use the medical data in an efficient way. This paper aims to improve the HF prediction accuracy using UCI heart disease dataset. For this, multiple machine learning approaches used to understand the data and predict the HF chances in a medical database. Furthermore, the results and comparative study showed that, the current work improved the previous accuracy score in predicting heart disease. The integration of the machine learning model presented in this study with medical information systems would be useful to predict the HF or any other disease using the live data collected from patients.

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“…V.V. Ramalingam et.al(2018) [2] investigated the prediction of heart disease based on each of the below mentioned algorithms i.e., Naïve Bayes , SVM, Decision Tree ,KNN and concluded by saying that ensemble models have performed extremely well in some cases but poorly in some other cases.Models based on Naïve Bayes classifier were computationally very fast and have also performed well.…”

Section: IImentioning

confidence: 99%

Effective Heart Disease Prediction Model Through Voting Technique

B¹

2020

IJETMS

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Machine learning has various practical applications that solves many issues relating to various domains .One among such domain is the health care domain and the most common application of machine learning is the prediction of an outcome based upon existing data in health care industry. Machine learning is shown as an effective technique in assisting the health care industry to make intelligent and effective decisions. The model tries to learn pattern from the existing dataset and later on it is applied to the unknown dataset for effectively predicting the outcome. Classification is the most effective technique for prediction of outcome. There are many classification algorithms which are used for prediction but only few algorithms predict with good accuracy whereas remaining algorithms predict with less accuracy. So to improve the accuracy of weak algorithms this paper presented a new method called ensemble classification ,where the accuracy is enhanced by combining multiple classifiers and later prediction is done by voting technique. So, experiments were done on a heart disease dataset, through ensemble approach the accuracy was enhanced and along with that a GUI was developed where the user himself can check whether he has probability of getting heart disease or not. The results of the study showed that ensemble method such as voting technique played a key role in improving the accuracy prediction of weak classifiers and also identified risk factors for occurrence of heart disease. An accuracy of 90% was achieved with voting technique and the performance of the process was further enhanced with a feature selection implementation, and the results showed significant improvement in prediction accuracy.

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Heart disease prediction using machine learning techniques : a survey

Cited by 246 publications

References 14 publications

Comparison of Support Vector Machine, Naïve Bayes and Logistic Regression for Assessing the Necessity for Coronary Angiography

Comparison of Support Vector Machine, Naïve Bayes and Logistic Regression for Assessing the Necessity for Coronary Angiography

Implementation of Machine Learning Model to Predict Heart Failure Disease

Effective Heart Disease Prediction Model Through Voting Technique

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