scholarly journals Computational Learning Model for Prediction of Heart Disease Using Machine Learning Based on a New Regularizer

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Abdulaziz Albahr ◽  
Marwan Albahar ◽  
Mohammed Thanoon ◽  
Muhammad Binsawad
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Standard Deviation ◽  
Early Diagnosis ◽  
Cross Validation ◽  
Heart Diseases ◽  
Computational Learning ◽  
Diagnosis And Prognosis ◽  
Tenfold Cross Validation ◽  
Better Than

Heart diseases are characterized as heterogeneous diseases comprising multiple subtypes. Early diagnosis and prognosis of heart disease are essential to facilitate the clinical management of patients. In this research, a new computational model for predicting early heart disease is proposed. The predictive model is embedded in a new regularization based on decaying the weights according to the weight matrices’ standard deviation and comparing the results against its parents (RSD-ANN). The performance of RSD-ANN is far better than that of the existing methods. Based on our experiments, the average validation accuracy computed was 96.30% using either the tenfold cross-validation or holdout method.

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 Determination of Significant Features for Building an Efficient Heart Disease Prediction System

2019 ◽  
Vol 8 (2) ◽  
pp. 4499-4504
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Model ◽  
Prediction Models ◽  
Heart Diseases ◽  
Medical Diagnostics ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
Prediction System ◽  
Early Stages

Heart diseases are responsible for the greatest number of deaths all over the world. These diseases are usually not detected in early stages as the cost of medical diagnostics is not affordable by a majority of the people. Research has shown that machine learning methods have a great capability to extract valuable information from the medical data. This information is used to build the prediction models which provide cost effective technological aid for a medical practitioner to detect the heart disease in early stages. However, the presence of some irrelevant and redundant features in medical data deteriorates the competence of the prediction system. This research was aimed to improve the accuracy of the existing methods by removing such features. In this study, brute force-based algorithm of feature selection was used to determine relevant significant features. After experimenting rigorously with 7528 possible combinations of features and 5 machine learning algorithms, 8 important features were identified. A prediction model was developed using these significant features. Accuracy of this model is experimentally calculated to be 86.4%which is higher than the results of existing studies. The prediction model proposed in this study shall help in predicting heart disease efficiently.

Analysis of Machine Learning Algorithms in Health Care to Predict Heart Disease

2019 ◽  
pp. 191-207
Author(s):  
P. Priyanga ◽  
N. C. Naveen
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Polynomial Kernel ◽  
K Nearest Neighbor ◽  
Aged Patients ◽  
Learning Machine

This article describes how healthcare organizations is growing increasingly and are the potential beneficiary users of the data that is generated and gathered. From hospitals to clinics, data and analytics can be a very powerful tool that can improve patient care and satisfaction with efficiency. In developing countries, cardiovascular diseases have a huge impact on increasing death rates and are expected by the end of 2020 in spite of the best clinical practices. The current Machine Learning (ml) algorithms are adapted to estimate the heart disease risks in middle aged patients. Hence, to predict the heart diseases a detailed analysis is made in this research work by taking into account the angiographic heart disease status (i.e. ≥ 50% diameter narrowing). Deep Neural Network (DNN), Extreme Learning Machine (elm), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) learning algorithm (with linear and polynomial kernel functions) are considered in this work. The accuracy and results of these algorithms are analyzed by comparing the effectiveness among them.

scholarly journals Heart Disease Prediction Using Machine Learning

2021 ◽  
pp. 267-276
Author(s):  
Baban. U. Rindhe ◽  
Nikita Ahire ◽  
Rupali Patil ◽  
Shweta Gagare ◽  
Manisha Darade
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Heart Disease ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Whole Body ◽  
Support Vector ◽  
Learning Techniques

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 fora 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. Heart is the next major organ comparing to the brain which has more priority in the Human body. It pumps the blood and supplies it to all organs of the whole body. Prediction of occurrences of heart diseases in the medical field is significant work. Data analytics is useful for prediction from more information and it helps the medical center to predict various diseases. A huge amount of patient-related data is maintained on monthly basis. The stored data can be useful for the source of predicting the occurrence of future diseases. Some of the data mining and machine learning techniques are used to predict heart diseases, such as Artificial Neural Network (ANN), Random Forest,and Support Vector Machine (SVM).Prediction and diagnosingof heart disease become a challenging factor faced by doctors and hospitals both in India and abroad. To reduce the large scale of deaths from heart diseases, a quick and efficient detection technique is to be discovered. Data mining techniques and machine learning algorithms play a very important role in this area. The researchers accelerating their research works to develop software with thehelp of machine learning algorithms which can help doctors to decide both prediction and diagnosing of heart disease. The main objective of this research project is to predict the heart disease of a patient using machine learning algorithms.

scholarly journals Identification and Implementation Perspective on the Stratification Algorithms in the Prognostication of Heart Disease using Machine Learning Techniques

2020 ◽  
Vol 9 (3) ◽  
pp. 1223-1225
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Heart Diseases ◽  
Classification Algorithm ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Learning Techniques ◽  
Machine Learning Model ◽  
Combination Of Classifiers ◽  
Great Idea

Analysis of patient’s data is always a great idea to get accurate results on using classifiers. A combination of classifiers would give an accurate result than using a single classifier because one single classifier does not give accurate results but always appropriate ones. The aim is to predict the outcome feature of the data set. The “outcome” can contain only two values that is 0 and 1. 0 means patient doesn’t have heart disease and 1 means patient have heart diseases. So, there is a need to build a classification algorithm that can predict the Outcome feature of the test dataset with good accuracy. For this understanding the data is important, and then various classification algorithm can be tested. Then the best model can be selected which gives highest accuracy among all. The built model can then be given to the software developer for building the end user application using the selected machine learning model that will be able to predict the heart disease in a patient.

scholarly journals Usage of Data Mining Techniques in Predicting the Heart Diseases Decision Tree & Random Forest Algorithm

2019 ◽  
Vol 9 (2) ◽  
pp. 963-967
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Random Forest ◽  
Early Diagnosis ◽  
Decision Tree ◽  
Heart Diseases ◽  
Classification Algorithms ◽  
Random Forest Algorithm ◽  
Medical Field ◽  
Data Mining Techniques

Nowadays, heart disease is the main cause of several deaths among all other diseases. Due to the lack of resources in the medical field, the prediction of heart diseases becomes a major problem. For early diagnosis and treatment, some classification algorithms such as Decision Tree and Random Forest Algorithm are used. The data mining techniques compare the accuracy of the algorithm and predict heart diseases. The main aim of this paper is to predict heart disease based on the dataset values. In this paper we are comparing the accuracy of above two algorithms. To implement these methods the following steps are used. In first phase, a dataset of 13 attributes is collected and it was applied on classification techniques using the Decision tree and Random Forest Algorithms. Finally, the accuracy is collected for both the algorithms. In this paper we observed that random forest is generating better results than decision tree in prediction of heart diseases.

Coronary Heart Disease Prognosis Using Machine-Learning Techniques on Patients With Type 2 Diabetes Mellitus

2019 ◽  
pp. 198-217
Author(s):  
Angela Pimentel ◽  
Hugo Gamboa ◽  
Isa Maria Almeida ◽  
Pedro Matos ◽  
Rogério T. Ribeiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Heart Diseases ◽  
Machine Learning Techniques ◽  
Health Authorities ◽  
Learning Techniques ◽  
Alternating Decision Tree

Heart diseases and stroke are the number one cause of death and disability among people with type 2 diabetes (T2D). Clinicians and health authorities for many years have expressed interest in identifying individuals at increased risk of coronary heart disease (CHD). Our main objective is to develop a prognostic workflow of CHD in T2D patients using a Holter dataset. This workflow development will be based on machine learning techniques by testing a variety of classifiers and subsequent selection of the best performing system. It will also assess the impact of feature selection and bootstrapping techniques over these systems. Among a variety of classifiers such as Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Alternating Decision Tree (ADT), Random Tree (RT) and K-Nearest Neighbour (KNN), the best performing classifier is NB. We achieved an area under receiver operating characteristics curve (AUC) of 68,06% and 74,33% for a prognosis of 3 and 4 years, respectively.

scholarly journals Improving the Accuracy for Analyzing Heart Diseases Prediction Based on the Ensemble Method

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiao-Yan Gao ◽  
Abdelmegeid Amin Ali ◽  
Hassan Shaban Hassan ◽  
Eman M. Anwar
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Ensemble Learning ◽  
Heart Diseases ◽  
Principal Component ◽  
Extraction Methods ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Linear Discriminant ◽  
Bagging Ensemble

Heart disease is the deadliest disease and one of leading causes of death worldwide. Machine learning is playing an essential role in the medical side. In this paper, ensemble learning methods are used to enhance the performance of predicting heart disease. Two features of extraction methods: linear discriminant analysis (LDA) and principal component analysis (PCA), are used to select essential features from the dataset. The comparison between machine learning algorithms and ensemble learning methods is applied to selected features. The different methods are used to evaluate models: accuracy, recall, precision, F-measure, and ROC.The results show the bagging ensemble learning method with decision tree has achieved the best performance.

scholarly journals A new smart healthcare framework for real-time heart disease detection based on deep and machine learning

2021 ◽  
Vol 7 ◽  
pp. e646
Author(s):  
Haitham Elwahsh ◽  
Engy El-shafeiy ◽  
Saad Alanazi ◽  
Medhat A. Tawfeek
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Real Time ◽  
Heart Diseases ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Medical Decision ◽  
Rate Condition ◽  
Smart Healthcare ◽  
Learning Techniques

Cardiovascular diseases (CVDs) are the most critical heart diseases. Accurate analytics for real-time heart disease is significant. This paper sought to develop a smart healthcare framework (SHDML) by using deep and machine learning techniques based on optimization stochastic gradient descent (SGD) to predict the presence of heart disease. The SHDML framework consists of two stage, the first stage of SHDML is able to monitor the heart beat rate condition of a patient. The SHDML framework to monitor patients in real-time has been developed using an ATmega32 Microcontroller to determine heartbeat rate per minute pulse rate sensors. The developed SHDML framework is able to broadcast the acquired sensor data to a Firebase Cloud database every 20 seconds. The smart application is infectious in regard to displaying the sensor data. The second stage of SHDML has been used in medical decision support systems to predict and diagnose heart diseases. Deep or machine learning techniques were ported to the smart application to analyze user data and predict CVDs in real-time. Two different methods of deep and machine learning techniques were checked for their performances. The deep and machine learning techniques were trained and tested using widely used open-access dataset. The proposed SHDML framework had very good performance with an accuracy of 0.99, sensitivity of 0.94, specificity of 0.85, and F1-score of 0.87.

scholarly journals Early Diagnosis of Congenital Heart Disease Improves Outcome

2020 ◽  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Early Diagnosis ◽  
Congenital Heart ◽  
Autosomal Recessive ◽  
Heart Diseases ◽  
Recessive Gene ◽  
Contributory Factor ◽  
Surgical Expertise

Congenital heart disease has an overall incidence of 8-10/1000 live births and is similar across the globe. The incidence may be higher in countries where consanguinity is high, suggesting an autosomal recessive gene as a risk factor. Advances in surgical expertise has improved outcome of simple and complex heart diseases. Early diagnosis and referral to centers caring for such babies is an important contributory factor to a better outcome. This review focuses on early diagnosis of congenital heart disease in neonates and children by Health Care Physicians (General and Family Physicians) and Pediatricians. Careful neonatal and pediatric cardiovascular examination, screening pulse oximetry on all newborns before hospital discharge and an early post natal follow up are important to diagnose CHD.

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