Prediction of heart disease using hybrid technique for selecting features

Pahwa, Kanika; Kumar, Ravinder

doi:10.1109/upcon.2017.8251100

Cited by 52 publications

(20 citation statements)

References 6 publications

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“…An accuracy of 84.1584% has been achieved in Naïve Bayes with 10 most important predictors chosen using SVM-RFE [7]. While, the accuracy of 83.49% has been achieved using all 13 attributes of Cleveland dataset [8].…”

Section: Algorithms Used Naïve Bayes' Classifiermentioning

confidence: 96%

Heart Disease Prediction using Machine Learning Techniques

2020

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Heart disease, alternatively known as cardiovascular disease, encases various conditions that impact the heart and is the primary basis of death worldwide over the span of the past few decades. It associates many risk factors in heart disease and a need of the time to get accurate, reliable, and sensible approaches to make an early diagnosis to achieve prompt management of the disease. Data mining is a commonly used technique for processing enormous data in the healthcare domain. Researchers apply several data mining and machine learning techniques to analyse huge complex medical data, helping healthcare professionals to predict heart disease. This research paper presents various attributes related to heart disease, and the model on basis of supervised learning algorithms as Naïve Bayes, decision tree, K-nearest neighbor, and random forest algorithm. It uses the existing dataset from the Cleveland database of UCI repository of heart disease patients. The dataset comprises 303 instances and 76 attributes. Of these 76 attributes, only 14 attributes are considered for testing, important to substantiate the performance of different algorithms. This research paper aims to envision the probability of developing heart disease in the patients. The results portray that the highest accuracy score is achieved with K-nearest neighbor.

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Section: Algorithms Used Naïve Bayes' Classifiermentioning

confidence: 96%

Heart Disease Prediction using Machine Learning Techniques

2020

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“…The study evaluated a hybrid approach using NB and Random Forest for diagnosis of heart disease. Cleveland dataset was used with 14 attributes [13]. The experiments were carried out using different set of features, 6,8,10 and 12 on Naïve Bayes and Random Forest.…”

Section: Related Workmentioning

confidence: 99%

Heart Disease Prediction using Ensemble Methods

Deshmukh*¹

2019

IJRTE

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Nowadays, people are suffering from many health issues. One of them is heart disease among the worldwide population. This causes due to imbalance lifestyle and unhealthy food consumption. The data generated by hospitals is huge and complex by nature which store patients medical and demographic information. Accurate and prompt diagnosis of heart diseases are becoming more challenging task in medical domain due to the complex data. Therefore, the computer aided systems are useful to store this complex and multivariate data to generate useful decisions. Machine learning techniques are used to classify and to predict the diseases. In this study, Majority voting classifier and Bagging ensemble method both have been evaluated. These ensemble methods combined the five base classifiers including DT (Decision Tree), LR (Logistic Regression), ANN (Artificial Neural Network), NB (Naïve Bayesian), and KNN (K-Nearest Neighbour). Bagging ensemble approach is used to combine the multiple classifiers prediction abilities for better performance. Experimental work is performed on Cleveland dataset using 14 attributes which is available online on UCI Repository. The results showed that the Bagging ensemble method is performed better to achieve higher accuracy of 87.78 %.

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“…In [7], Naive Bayes has achieved an accuracy of 84.1584% with the 10 most significant features which are selected using SVM-RFE (Recursive Feature Elimination) and gain ratio algorithms whereas in [8],Naive Bayes has achieved an accuracy of 83.49% when all 13 attributes of the Cleveland dataset [25] are used.…”

Section: A Naïve Bayesmentioning

confidence: 99%

Heart disease prediction using machine learning techniques : a survey

Ramalingam¹,

Dandapath²,

Raja³

2018

IJET

246

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Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

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Prediction of heart disease using hybrid technique for selecting features

Cited by 52 publications

References 6 publications

Heart Disease Prediction using Machine Learning Techniques

Heart Disease Prediction using Machine Learning Techniques

Heart Disease Prediction using Ensemble Methods

Heart disease prediction using machine learning techniques : a survey

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