Heart Disease Prediction Model based Ongradient Boosting Tree (GBT) Classification Algorithm

Recently, Heart disease (HD) is the main cause of increasing death rate all over the world. Data classification is a crucial task in the medical field which assists the physicians to predict the diseases. Recently, machine learning (ML) algorithms have been employed to classify the data in the medical field. The data complexity and quantity needs to be examined and managed to transform the efficient and accurate HD diagnosis. In this paper, a gradient boosting tree (GBT) based classifier or gradient boosting classifier (GBC) model to predict the HD efficiently. Besides, a set of extensive experiments were carried out using Staglog and Cleveland heart disease dataset. The experimental values ensured the superiority of the GBT classifier based on several performance measures.

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

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.l3473.1081219 ◽

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.

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Heat Disease Prediction using Machine Learning

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.36372 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 846-852

Author(s):

Prof. Dr. R. Sandhiya

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Data Science ◽

Machine Learning Algorithms ◽

Assessment Process ◽

Disease Prediction ◽

Test Results ◽

Medical Field ◽

Modern Age ◽

Linear Svm

In recent times, the diagnosis of heart disease has become a very critical task in the medical field. In the modern age, one person dies every minute due to heart disease. Data science has an important role in processing big amounts of data in the field of health sciences. Since the diagnosis of heart disease is a complex task, the assessment process should be automated to avoid the risks associated with it and alert the patient in advance. This paper uses the heart disease dataset available in the UCI Machine Learning Repository. The proposed work assesses the risk of heart disease in a patient by applying various data mining methods such as Naive Bayes, Decision Tree, KNN, Linear SVM, RBF SVM, Gaussian Process, Neural Network, Adabost, QDA and Random Forest. This paper provides a comparative study by analyzing the performance of various machine learning algorithms. Test results confirm that the KNN algorithm achieved the highest 97% accuracy compared to other implemented ML algorithms.

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Heart Failure Prognostication using Boosting Algorithms

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35336 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 1964-1972

Author(s):

Rachaell Nihalaani

Keyword(s):

Heart Failure ◽

Heart Disease ◽

Performance Measures ◽

Public Library ◽

Gradient Boosting ◽

Medical Field ◽

Boosting Algorithms ◽

Do So

In the medical field, predicting a heart disease has become a very complicated and challenging task. So, in this contemporary lifestyle, there is an urgent need for a system that will help predict accurately the possibility of getting heart disease. This paper presents an observation-based comparison between four boosting algorithms namely Gradient boosting, XGBoost, ADAboost and CatBoost to predict heart failure efficiently. To do so, we have referred to the PLOS (Public Library of Science) Repository dataset. These algorithm’s performances have been evaluated using metrics like Accuracy, F1 score, Recall and many more. All values obtained ensured the superiority of these boosting algorithms based on several performance measures.

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A Comprehensive survey on Heart Disease Prediction using Machine Intelligence

10.21203/rs.3.rs-680505/v1 ◽

2021 ◽

Author(s):

Santhosh Gupta Dogiparthi ◽

Jayanthi K ◽

Ajith Ananthakrishna Pillai

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Prediction Models ◽

Machine Intelligence ◽

World Health ◽

World Population ◽

Disease Prediction ◽

Validation Parameters ◽

The World ◽

Comprehensive Survey

Abstract Objectives: The latest statistics of World Health Organization anticipated that cardiovascular diseases including Coronary Heart Disease, Heart attack, vascular disease as the biggest pandemic to the world due to which one-third of the world population would die. With the emerging AI trends, applying an optimal machine learning model to target early detection and accurate prediction of heart disease is indispensable to bring down the mortality rates and to treat the cardiac patients with best clinical decision support. This stems for the motivation of this paper. This paper presents a comprehensive survey on heart disease prediction models derived and validated out of popular heart disease datasets like Cleveland dataset, Z-Alizadeh Sani dataset. Methods: This survey was performed using the articles extricated from the Google Scholar, Scopus, Web of Science, Research Gate and PubMed search engines between 2005 to 2020. The main keywords for search were Heart Disease, Prediction, Coronary disease, Healthcare, Heart datasets and Machine Learning.Results: This review explores the shortcomings of various approaches used for the prediction of heart diseases. It outlines pros and cons of different research methodologies along with the validation parameters of each reviewed publication.Conclusion: The machine intelligence can serve as a genuine alternative diagnostic method for prediction, which will in turn keep the patients well aware of their illness state. Despite the researcher’s efforts, still uncertainty exist towards standardization of prediction models which demands further exploration of optimal prediction models.

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Heart Disease Prediction System Using Ensemble of Machine Learning Algorithms

Recent Patents on Engineering ◽

10.2174/1872212113666190328220514 ◽

2019 ◽

Vol 13 ◽

Author(s):

Nandhini Abirami R. ◽

Durai Raj Vincent

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Random Forest ◽

Learning Algorithms ◽

Medical Intervention ◽

Machine Learning Algorithms ◽

Disease Prediction ◽

Prediction System ◽

Random Forest Algorithm ◽

Medical Field

Background: Diagnosing diseases is an intricate job in medical field. Machine learning when applied to health care is capable of early detection of disease which would aid to provide early medical intervention. In heart disease prediction, machine learning techniques have played a significant role. Analysis of disease has become vital in health care sectors. The massive data collected by healthcare sectors are preprocessed and analyzed to discover the underlying information in the data for effective decision making and to provide proper medical intervention. The success of machine learning in medical industry is its capability in analyzing the huge amount of data gathered by the health sector and its effectiveness in decision making. Since medical field involves too many manual processes it has become necessary to automate these procedures. Remarkable advancements in electronic medical records have made it possible. Diagnosing diseases is an intricate job in medical field. Objective: The objective of this research is to design a robust machine learning algorithm to predict heart disease. The prediction of heart disease is performed using Ensemble of machine learning algorithms. This is to boost the accuracy achieved by individual machine learning algorithms. Method: Heart Disease Prediction System is developed where the user can input the patient details and the prediction for the particular patient is made using the model developed. The model will predict the output to be either normal or risky. Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), Support Vector Machines (SVM), K-Nearest Neighbors (KNN) and Naïve Bayes classifier are used as base learners. These algorithms are combined using random forest as the meta classifier. Results: The predictions of classifier are combined using random forest algorithm. The accuracy is lifted from 85.53% to 87.64% which is an impressive improvement on accuracy. Conclusion: Various techniques were adopted to preprocess the data to suite the requirement of analysis. Feature selections were made to optimize the performance of machine learning algorithms. Ensemble prediction gave better accuracy when combined using Random forest algorithm as combiner. Better feature selection techniques can be applied to further improve the accuracy.

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Disease Prediction using Machine Learning

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f9241.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 3906-3911

Keyword(s):

Machine Learning ◽

Linear Regression ◽

Life Expectancy ◽

Death Rate ◽

Causes Of Death ◽

Disease Prediction ◽

Standards Of Living ◽

The World ◽

Regression Algorithms ◽

Global Population

As global population is increasing, life expectancy rises, and standards of living increases, and the causes of death across the world are changing. Now-a-days most of the deaths are occurring due to the lack of awareness among people about which disease may have the chance to occur for a particular symptom. So by taking the previous datasets of patients and analyzing it, we can predict which disease may have the scope to occur for a particular symptom. It creates an awareness among people about the disease and appropriate medication can be received. By this the death rate can be minimized. So, in this system, we are going to analyse the patient datasets by using machine-learning(linear regression) algorithms and python code to predict the diseases which have the chance to occur and so that we can reduce the death rate

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Predicting Heart Disease at Early Stages using Machine Learning: A Survey

Journal of University of Shanghai for Science and Technology ◽

10.51201/jusst/21/09662 ◽

2021 ◽

Vol 23 (09) ◽

pp. 1178-1181

Author(s):

Mr. Sumit Hawal ◽

◽

Dr. Sandeep Dwarkanath Pande ◽

Keyword(s):

Machine Learning ◽

Cardiovascular Disease ◽

Heart Disease ◽

Early Stage ◽

Disease Diagnosis ◽

Primary Objective ◽

Accurate Diagnosis ◽

Disease Prediction ◽

Medical Field ◽

Critical Machine

Cardiovascular disease diagnosis is the most difficult task in medicine. The diagnosis of heart disease is complicated because it requires the grouping of massive volumes of clinical and pathological data. As a result of this dilemma, researchers and clinical professionals have developed a strong interest in the efficient and exact prediction of heart disease. When it comes to heart disease, it is critical to obtain an accurate diagnosis at an early stage because time is of the essence. Heart disease is the largest cause of death worldwide, and early detection of heart disease is critical. Machine learning has evolved as one of the most progressive, dependable, and supportive tools in the medical field in recent years, providing the greatest assistance for disease prediction when properly trained and tested. The primary objective of this research is to evaluate several algorithms for heart disease prediction.

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