scholarly journals Recommendation of Attributes for Heart Disease Prediction using Correlation Measure

2019 ◽  
Vol 8 (2S3) ◽  
pp. 870-875
Keyword(s):  
Heart Disease ◽  
Mortality Rate ◽  
Early Stage ◽  
Heart Diseases ◽  
Correct Diagnosis ◽  
Disease Prediction ◽  
Correlation Measure ◽  
Heart Problem ◽  
Correlation Measures ◽  
General Prediction

Heart diseases are the major cause for human mortality rate. Correct diagnosis and treatment at an early stage will save people from heart disease and will decrease mortality rate due to heart problem. Since ten years various data mining techniques have been used to facilitate the prediction of heart diseases .In general prediction algorithms for trained with huge, known dataset to arrive at a classifier which then predicts the diseases for unknown data with the help of classifying attributes. These attributes also called as features. In this work relevant features are determined for heart disease prediction with known dataset using correlation measures. The results are presented.

Heart Disease Prediction Using Machine Learning

2021 ◽  
pp. 373-381
Author(s):  
Shiva Shanta Mani B. ◽  
Manikandan V. M.
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Early Stage ◽  
Heart Diseases ◽  
Age Groups ◽  
Disease Prediction ◽  
Learning Approaches ◽  
Health Issues ◽  
Decision Tree Classifier ◽  
Knn Classifier

Heart disease is one of the most common and serious health issues in all the age groups. The food habits, mental stress, smoking, etc. are a few reasons for heart diseases. Diagnosing heart issues at an early stage is very much important to take proper treatment. The treatment of heart disease at the later stage is very expensive and risky. In this chapter, the authors discuss machine learning approaches to predict heart disease from a set of health parameters collected from a person. The heart disease dataset from the UCI machine learning repository is used for the study. This chapter discusses the heart disease prediction capability of four well-known machine learning approaches: naive Bayes classifier, KNN classifier, decision tree classifier, random forest classifier.

scholarly journals Study of Machine Learning Algorithms for Prediction and Diagnosis of Cardiovascular Diseases : A Review

2020 ◽  
pp. 480-489
Author(s):  
Manoj Patil ◽  
Harsh Mathur
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Heart Disease ◽  
Early Stage ◽  
Correct Diagnosis ◽  
Machine Learning Algorithms ◽  
Knowledge Discovery In Databases ◽  
Disease Prediction ◽  
Using Data ◽  
Modern Era

We are living in a post modern era and there are tremendous changes happening to our daily life which make an impact on our health positively and negatively. As a result of these changes various kind of diseases are enormously increased. In the medical field, the diagnosis of cardiovascular disease is the most difficult task. The diagnosis of cardiovascular disease is difficult as a decision relied on grouping of large clinical and pathological data. Due to this complication, the interest increased in a significant amount between the researchers and clinical professionals about the efficient and accurate heart disease prediction. In case of heart disease, the correct diagnosis in early stage is important as time is the very important factor. Heart disease is the principal source of deaths widespread, and the prediction of Heart Disease is significant at an untimely phase. Machine learning in recent years has been the evolving, reliable and supporting tools in medical domain and has provided the greatest support for predicting disease with correct case of training and testing. This research paper intends to provide a survey of techniques of knowledge discovery in databases using data mining techniques that are in use in today’s medical research particularly in Cardiovascular Disease Prediction.

scholarly journals Treatment of cyanotic heart diseases in children

2004 ◽  
Vol 132 (suppl. 1) ◽  
pp. 9-13
Author(s):  
Ida Jovanovic ◽  
Vojislav Parezanovic ◽  
Slobodan Ilic ◽  
Djordje Hercog ◽  
Milan Vucicevic ◽  
...  
Keyword(s):  
Heart Disease ◽  
Mortality Rate ◽  
Surgical Procedures ◽  
Heart Surgery ◽  
Heart Diseases ◽  
Arterial Switch Operation ◽  
Open Heart Surgery ◽  
Age Groups ◽  
Mortality Rates ◽  
Cyanotic Heart Disease

Cyanotic heart diseases are relatively rare, but they are severe and heterogeneous congenital heart diseases, which require complex surgery. Development of different advanced surgical procedures, such as arterial switch operation (ASO), Fontan and its modifications, Norwood etc. operations, as well as better perioperative care significantly improved survival rate and quality of life of these children. The study group included 308 children treated for cyanotic heart disease in Yugoslavia, in the period January 2000 to July 2004. Some of them (239, 77.6%) were treated at the University Children?s Hospital in Belgrade, and others (69, 22.4%) in different institutions abroad. The age of the operated patients varied between 1 day and 19 years (median 12 months). The patients (pts) were divided into four groups, according to the disease and type of the operation. In the whole group of 308 patients treated due to cyanotic heart disease, there were 232 (75.3%) cases with open heart surgery and 76 (24.7%) with closed procedures. The mortality rate was significantly different between disease/operation groups, and age groups. Average mortality rates differed from 11.8% for palliative procedures to 12.5% for complete corrections. Mortality rate and achieved surgical results in treatment of chil?dren with cyanotic heart diseases were significantly worse than those published by leading cardiac surgery centers in the world. However, there is a clear tendency in introducing new surgical procedures, lowering the age at which the operation is done and decreasing the mortality rates.

scholarly journals Ensembling Coalesce of Logistic Regression Classifier for Heart Disease Prediction using Machine Learning

2019 ◽  
Vol 8 (12) ◽  
pp. 127-133
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Heart Disease ◽  
Heart Diseases ◽  
Experimental Results ◽  
Disease Prediction ◽  
Feature Importance ◽  
The World ◽  
Feature Scaling ◽  
Logistic Regression Classifier

In today’s modern world, the world population is affected with some kind of heart diseases. With the vast knowledge and advancement in applications, the analysis and the identification of the heart disease still remain as a challenging issue. Due to the lack of awareness in the availability of patient symptoms, the prediction of heart disease is a questionable task. The World Health Organization has released that 33% of population were died due to the attack of heart diseases. With this background, we have used Heart Disease Prediction dataset extracted from UCI Machine Learning Repository for analyzing and the prediction of heart disease by integrating the ensembling methods. The prediction of heart disease classes are achieved in four ways. Firstly, The important features are extracted for the various ensembling methods like Extra Trees Regressor, Ada boost regressor, Gradient booster regress, Random forest regressor and Ada boost classifier. Secondly, the highly importance features of each of the ensembling methods is filtered from the dataset and it is fitted to logistic regression classifier to analyze the performance. Thirdly, the same extracted important features of each of the ensembling methods are subjected to feature scaling and then fitted with logistic regression to analyze the performance. Fourth, the Performance analysis is done with the performance metric such as Mean Squared error (MSE), Mean Absolute error (MAE), R2 Score, Explained Variance Score (EVS) and Mean Squared Log Error (MSLE). The implementation is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that before applying feature scaling, the feature importance extracted from the Ada boost classifier is found to be effective with the MSE of 0.04, MAE of 0.07, R2 Score of 92%, EVS of 0.86 and MSLE of 0.16 as compared to other ensembling methods. Experimental results shows that after applying feature scaling, the feature importance extracted from the Ada boost classifier is found to be effective with the MSE of 0.09, MAE of 0.13, R2 Score of 91%, EVS of 0.93 and MSLE of 0.18 as compared to other ensembling methods.

scholarly journals A Genetic Algorithm based Hybrid Deep Learning Approach for Heart Disease Prediction

2021 ◽  
Vol 16 (3) ◽  
Author(s):  
Khushbu Verma ◽  
Ankit Singh Bartwal ◽  
Mathura Prasad Thapliyal
Keyword(s):  
Genetic Algorithm ◽  
Heart Disease ◽  
Deep Learning ◽  
Early Stage ◽  
Support Vector ◽  
Disease Prediction ◽  
The Past ◽  
Lifestyle Choices ◽  
Proposed Model ◽  
Tree Algorithms

People nowadays suffer from a variety of heart ailments as a result of the environment and their lifestyle choices. As a result, analyzing sickness at an early stage becomes a critical responsibility. Data mining uses disease data to uncover important knowledge. In this research paper, we employ the hybrid combination of a Genetic Algorithm based Feature selection and Ensemble Deep Neural Network Model for Heart Disease prediction. In this algorithm, we used a 0.04 learning rate and Adam optimizer was used for enhancement of the proposed model. The proposed algorithm has come to 98% accuracy of heart disease prediction, which is higher than the past approaches. Other exist models such as random forest, logistic regression, support vector machine, Decision tree algorithms have taken a higher time and give less accuracy compare to the proposed hybrid deep learning-based approach.

scholarly journals Heart Disease Diagnosis by Neural Networks

2021 ◽  
pp. 202-208
Author(s):  
Khyati Varshney ◽  
Mrinal Paliwal
Keyword(s):  
Neural Networks ◽  
Heart Disease ◽  
Mortality Rate ◽  
Intelligent Systems ◽  
Heart Diseases ◽  
Disease Diagnosis ◽  
Daily Basis ◽  
The World ◽  
Multilayer Neural Networks ◽  
Heart Disease Diagnosis

In the present time the Mortality rate will be increased all around the world on their daily basis. So the cause for this might possibly be largely ascribe to the developing in the numbers of the patients with the cardiovascular patient’s diseases. To aggravate the cases, many physicians that have been known for the misdiagnosis of the patients announce heart related ailments. In this research paper, the intelligent systems have been designed in which they will help in the successful diagnosis of the forbearing to avoiding misdiagnosis. In the dataset of a UCI stat log of heart disease that will be using in this investigation. The dataset contains 14 attributes which are essential in the diagnosis of the heart diseases. A system is sculpted on the multilayer neural networks trained with convolutional & simulated convolutional neural networks. The identification of 89% was acquired from the testing of the networks.

scholarly journals Ensemble model for Heart Disease Prediction

2021 ◽  
Vol 1 (4) ◽  
pp. 268-280
Author(s):  
Bamanga Mahmud , , , Ahmad ◽  
Ahmadu Asabe Sandra ◽  
Musa Yusuf Malgwi ◽  
Dahiru I. Sajoh
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Model ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Ensemble Model ◽  
Prediction Probability

For the identification and prediction of different diseases, machine learning techniques are commonly used in clinical decision support systems. Since heart disease is the leading cause of death for both men and women around the world. Heart is one of the essential parts of human body, therefore, it is one of the most critical concerns in the medical domain, and several researchers have developed intelligent medical devices to support the systems and further to enhance the ability to diagnose and predict heart diseases. However, there are few studies that look at the capabilities of ensemble methods in developing a heart disease detection and prediction model. In this study, the researchers assessed that how to use ensemble model, which proposes a more stable performance than the use of base learning algorithm and these leads to better results than other heart disease prediction models. The University of California, Irvine (UCI) Machine Learning Repository archive was used to extract patient heart disease data records. To achieve the aim of this study, the researcher developed the meta-algorithm. The ensemble model is a superior solution in terms of high predictive accuracy and diagnostics output reliability, as per the results of the experiments. An ensemble heart disease prediction model is also presented in this work as a valuable, cost-effective, and timely predictive option with a user-friendly graphical user interface that is scalable and expandable. From the finding, the researcher suggests that Bagging is the best ensemble classifier to be adopted as the extended algorithm that has the high prediction probability score in the implementation of heart disease prediction.

scholarly journals Heart Disease Prediction and Performance Assessment through Attribute Element Diminution using Machine Learning

2019 ◽  
Vol 8 (11) ◽  
pp. 604-609
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Heart Disease ◽  
Random Forest ◽  
Heart Diseases ◽  
Support Vector ◽  
Disease Prediction ◽  
Data Set ◽  
Raw Data ◽  
Reduced Data

In today’s modern world, the human beings are affected with heart disease irrespective of the age. With the advancement of technological growth, predicting the availability of Heart diseases still remains a challenging issue. The difficulty of predicting the heart disease prevails due to the lack of availability of the symptoms. According to World Health Organization, 33% of population died due to heart diseases. For this, the diagnosis of heart diseases is made by complex combination of clinical data. With this overview, we have used Heart Disease Prediction dataset extracted from UCI Machine Learning Repository for predicting the level of heart disease. The prediction of heart disease classes are achieved in four ways. Firstly, the data set is preprocessed with Feature Scaling and Missing Values. Secondly, the raw data set is fitted to classifiers like logistic regression, KNN classifier, Support Vector Machine, Kernel Support Vector Machine, Naive Bayes, Random Forest and Decision Tree classifiers. Third, the raw data set is subjected to dimensionality reduction using Principal Component Analysis to project the dataset with important components. The dimensionality PCA reduced data set is fitted to the above-mentioned classifiers. Fourth, the performance comparison of raw data set and PCA reduced data set is done by analyzing the performance metrics like Precision, Recall, Accuracy and F-score. The implementation is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that Random forest is found to be effective with the accuracy of 89% without applying PCA, 85% with five component PCA and 86% with seven component PCA.

scholarly journals A novel approach for heart disease prediction using strength scores with significant predictors

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Armin Yazdani ◽  
Kasturi Dewi Varathan ◽  
Yin Kia Chiam ◽  
Asad Waqar Malik ◽  
Wan Azman Wan Ahmad
Keyword(s):  
Cardiovascular Disease ◽  
Heart Disease ◽  
Significant Contribution ◽  
Heart Diseases ◽  
Confidence Score ◽  
Disease Prediction ◽  
Clinical Tests ◽  
Rule Mining ◽  
The Past ◽  
Novel Approach

Abstract Background Cardiovascular disease is the leading cause of death in many countries. Physicians often diagnose cardiovascular disease based on current clinical tests and previous experience of diagnosing patients with similar symptoms. Patients who suffer from heart disease require quick diagnosis, early treatment and constant observations. To address their needs, many data mining approaches have been used in the past in diagnosing and predicting heart diseases. Previous research was also focused on identifying the significant contributing features to heart disease prediction, however, less importance was given to identifying the strength of these features. Method This paper is motivated by the gap in the literature, thus proposes an algorithm that measures the strength of the significant features that contribute to heart disease prediction. The study is aimed at predicting heart disease based on the scores of significant features using Weighted Associative Rule Mining. Results A set of important feature scores and rules were identified in diagnosing heart disease and cardiologists were consulted to confirm the validity of these rules. The experiments performed on the UCI open dataset, widely used for heart disease research yielded the highest confidence score of 98% in predicting heart disease. Conclusion This study managed to provide a significant contribution in computing the strength scores with significant predictors in heart disease prediction. From the evaluation results, we obtained important rules and achieved highest confidence score by utilizing the computed strength scores of significant predictors on Weighted Associative Rule Mining in predicting heart disease.

scholarly journals Comparative Analysis for Heart Disease Prediction

2017 ◽  
Vol 1 (4-2) ◽  
pp. 227
Author(s):  
Sundas Naqeeb Khan ◽  
Nazri Mohd Nawi ◽  
Asim Shahzad ◽  
Arif Ullah ◽  
Muhammad Faheem Mushtaq ◽  
...  
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Prediction Model ◽  
Prediction Models ◽  
Heart Diseases ◽  
Disease Prediction ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
Efficient Prediction ◽  
Causes Of Deaths

Today, heart diseases have become one of the leading causes of deaths in nationwide. The best prevention for this disease is to have an early system that can predict the early symptoms which can save more life. Recently research in data mining had gained a lot of attention and had been used in different kind of applications including in medical. The use of data mining techniques can help researchers in predicting the probability of getting heart diseases among susceptible patients. Among prior studies, several researchers articulated their efforts for finding a best possible technique for heart disease prediction model. This study aims to draw a comparison among different algorithms used to predict heart diseases. The results of this paper will helps towards developing an understanding of the recent methodologies used for heart disease prediction models. This paper presents analysis results of significant data mining techniques that can be used in developing highly accurate and efficient prediction model which will help doctors in reducing the number of deaths cause by heart disease.

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