scholarly journals Heart Disease Prediction System

2021 ◽  
Vol 9 (9) ◽  
pp. 1861-1865
Author(s):  
Anjana K.S
Keyword(s):  
Heart Disease ◽  
Early Stage ◽  
Heart Diseases ◽  
Vascular Diseases ◽  
Feature Reduction ◽  
Irregular Pattern ◽  
Ecg Arrhythmia Classification ◽  
Cardio Vascular ◽  
The One ◽  
Ecg Arrhythmia

Abstract: Heart diseases are the one of the primary reasons of human death today. There are many recent technologies are used to assist the medical professionals and doctors in the prediction of heart disease in the early stage. Prediction of heart disease is a critical challenge in the area of clinical data analysis. This paper introduces a technique to detect arrhythmia, which is a representative type of cardio vascular diseases. Arrhythmia refers to any irregular change from the normal heart rhythms, means that your heart beats too quickly, too slowly, or with an irregular pattern. The Electro Cardiogram (ECG) is used as an input for the arrhythmia detection. It displays the rhythm and status of the heart. This paper propose an effective ECG arrhythmia classification approach based on a deep convolutional neural network (CNN), which has lately demonstrated remarkable performance in the field of machine learning. It perform the classification without any manual pre-processing of the ECG signals such as noise filtering, feature extraction, and feature reduction. Keywords: Arrhythmia, ECG, deep learning, CNN, ResNet

A Review on Heart Disease Detection Techniques

2017 ◽  
Vol 7 (7) ◽  
pp. 395
Author(s):  
. Anika ◽  
Navpreet Kaur
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Review Paper ◽  
Vascular Diseases ◽  
Disease Detection ◽  
Automated Classification ◽  
Feature Extraction Method ◽  
Detection Techniques ◽  
Cardio Vascular ◽  
Public Domain Software

The paper exhibits a formal audit on early detection of heart disease which are the major cause of death. Computational science has potential to detect disease in prior stages automatically. With this review paper we describe machine learning for disease detection. Machine learning is a method of data analysis that automates analytical model building.Various techniques develop to predict cardiac disease based on cases through MRI was developed. Automated classification using machine learning. Feature extraction method using Cell Profiler and GLCM. Cell Profiler a public domain software, freely available is flourished by the Broad Institute's Imaging Platform and Glcm is a statistical method of examining texture .Various techniques to detect cardio vascular diseases.

scholarly journals Prediction of Heart Disease Using Machine Learning Algorithms

2018 ◽  
Vol 7 (2.32) ◽  
pp. 363 ◽  
Author(s):  
N Rajesh ◽  
Maneesha T ◽  
Shaik Hafeez ◽  
Hari Krishna
Keyword(s):  
Risk Factors ◽  
Heart Disease ◽  
Decision Trees ◽  
Naive Bayes ◽  
Heart Diseases ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Common Disease ◽  
Bayes Algorithm ◽  
The One

Heart disease is the one of the most common disease. This disease is quite common now a days we used different attributes which can relate to this heart diseases well to find the better method to predict and we also used algorithms for prediction. Naive Bayes, algorithm is analyzed on dataset based on risk factors. We also used decision trees and combination of algorithms for the prediction of heart disease based on the above attributes. The results shown that when the dataset is small naive Bayes algorithm gives the accurate results and when the dataset is large decision trees gives the accurate results.  

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.

scholarly journals Performance Analysis of ECG Arrhythmia Classification based on Different SVM Methods

2020 ◽  
Vol 9 (12) ◽  
pp. 45-49
Keyword(s):  
Support Vector Machine ◽  
Decision Function ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Sets ◽  
Artery Disease ◽  
Ecg Arrhythmia Classification ◽  
The One ◽  
Ecg Arrhythmia ◽  
Ecg Data

Heart arrhythmias are the different types of heartbeats which are irregular in nature. In Tachycardia the heartbeat works too fast and in case of Bradycardia it works too slow. In the study of different cardiac conditions automatic detection of heart arrhythmia is done by the classification and feature extraction of Electrocardiogram(ECG) data. Various Support Vector Machine based methods are used to analyze and classify ECG signals for arrhythmia detection. There are several Support Vector Machine (SVM) methods used to classify the ECG data such as one against all, one against one and fuzzy decision function. This classification detects the existence of the arrhythmia and it helps the physicians to treat the heart patient with more accurate way. To train SVM, the MIT BIH Arrhythmia database is used which works with the heart disorder like sinus bradycardy, old inferior myocardial infarction, coronary artery disease, right bundle branch block. All three methods are implemented in proper way, and their rate of accuracy with SVM classifier is optimal when it is processed with the one-against-all method. The data sets of ECG arrhythmia are usually complex in nature, so for the SVM based classification one-against-all method has great impact and will fetch better result.

scholarly journals Ecg Signal based Arrhythmia Detection System using Optimized Hybrid Classifier

2019 ◽  
Vol 8 (9) ◽  
pp. 2207-2212
Keyword(s):  
Search Algorithm ◽  
Detection System ◽  
Well Being ◽  
Optimization Techniques ◽  
Feature Reduction ◽  
Support Vector ◽  
Severe Problem ◽  
Hybrid Classifier ◽  
Ecg Arrhythmia Classification ◽  
Ecg Arrhythmia

An essential diagnostic tool in identifying heart rhythm irregularities, known as arrhythmias, is the ECG (Electrocardiogram). Accurate identification of arrhythmias in clinical environments is critical to patient well-being, as both acute and chronic heart conditions are typically reflected in these measurements. This is known to be a severe problem even for human experts, due to variability between individuals and inevitable noise. In this research, we have proposed an effective ECG arrhythmia classification method using a hybrid classifier with SVM (Support vector machine) and ANN (Artificial neural network) which recently shows outstanding performance in the field of pattern recognition. Every ECG beat was transformed into two-dimensional data as input data for the hybrid classifier. Optimization of the proposed hybrid classifier includes various optimization techniques such as GA (Genetic algorithm) and CS (Cuckoo search) algorithm with an optimal objective function. Also, we have compared our proposed hybrid classifier with wellknown optimized ANN based ECG arrhythmia classification models. ECG recordings from the MIT-BIH arrhythmia database are used for the evaluation of the classifier. To precisely validate the hybrid classifier, cross-validation was performed at the evaluation, which involves every ECG recording as a test data with GA and with CS. The experimental results have successfully validated that the proposed hybrid classifier with the GA and CS has achieved excellent classification accuracy without any requirement of manual pre-processing of the ECG signals such as noise filtering, feature extraction, and feature reduction.

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 Nitric oxide role in pathogenesis and treatment of cardio-vascular diseases (literature review)

2021 ◽  
Vol 25 (2(98)) ◽  
pp. 130-134
Author(s):  
S. Biletskyi
Keyword(s):  
Nitric Oxide ◽  
Heart Disease ◽  
Essential Hypertension ◽  
Cardiovascular Diseases ◽  
Vascular Diseases ◽  
No Donors ◽  
Comprehensive Therapy ◽  
Nitric Oxide Deficiency ◽  
Cardio Vascular

Literature data concerning the role of endothelium and nitric oxide in the pathogenesis of cardiovascular diseases, administration of L-arginine as a part of a comprehensive therapy of patients suffering from essential hypertension (EH) and ischemic heart disease (IHD) are cited.Objective: to systematize current literature data concerning the role of endothelium and nitric oxide in the pathogenesis of cardiovascular diseases, clinical experience of L-arginine administration in patients with EH and IHD. Conclusion. Nowadays endothelial dysfunction conception is defined with insufficient production of nitric oxide as a central part of EH and IHD pathogenesis. Nitric oxide deficiency occurring with cardiovascular diseases can be compensated by means of NO donors.

scholarly journals Prescribing Pattern in Geriatrics with Cardio Vascular Diseases using Beers Criteria

2021 ◽  
pp. 6-11
Author(s):  
E. Pavan Kumar ◽  
V. Sreedhar ◽  
P. Ramakrishna Reddy ◽  
L. Reddenna ◽  
M. Pramod Kumar ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Coronary Artery Disease ◽  
Heart Disease ◽  
Coronary Artery ◽  
Vascular Diseases ◽  
Beers Criteria ◽  
Prescribing Pattern ◽  
Male Patients ◽  
Artery Disease ◽  
Cardio Vascular

Aim: Cardiovascular disease (CVD) is a major health problem throughout the world and a common cause of premature morbidity and mortality. CVD is a general category of diseases that affects the heart and the circulatory system. The main aim of the study is to assess the prescribing pattern in geriatrics with cardiovascular diseases using beers criteria. Study Design: Prospective observational study. Results and Discussion: Total 132 patients, 12 dropouts due to lack of information. Out of 120 patients 69 Patients are identified as Male Patients and 51 Patients are Female. In 120 sample size Maximum No of Cases were found with Ischemic Heart disease (30.8%) Followed by myocardial infarction (24%) coronary artery disease (20%) congestive heart failure (13.3%) Unstable Angina (11.6%). In 120 Sample Size, Male Patients are Suffering More with Complications Compared to Female Patients. Conclusion: In this Study with Assessing the Prescribing Pattern in Geriatrics with Cardio Vascular Diseases It was found that major complications seen in Male and Female Patients are Ischemic heart Disease with Left ventricular dysfunction Myocardial Infarction, Coronary Artery Disease, Angina, Congestive Cardiac Failure.

scholarly journals PREVALENCE OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE IN PATIENTS WITH CORONARY HEART DISEASE AND ARTERIAL HYPERTENSION

2017 ◽  
Vol 2 ◽  
pp. 38-45 ◽  
Author(s):  
Lesya Rasputina ◽  
Daria Didenko
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Arterial Hypertension ◽  
Pulmonary Disease ◽  
Vascular Diseases ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Stable Coronary Heart Disease ◽  
Cardio Vascular

The prevalence of chronic obstructive pulmonary disease among patients with cardio-vascular diseases is higher than in general population. At the same time the one of problems of internal medicine is a timely diagnostics of chronic obstructive pulmonary disease. The aim of the work was the study of prevalence of chronic obstructive pulmonary disease among patients with cardio-vascular diseases, especially arterial hypertension and coronary heart disease. Materials and methods. The retrospective analysis of statistical cards of patients, who were on stationary treatment at therapeutic departments, was carried out to estimate the prevalence of combination of chronic obstructive pulmonary disease with arterial hypertension. The target examination of 136 patients was realized for revelation of chronic obstructive pulmonary disease. All patients were interrogated by the original modified questionnaire of assessment of short breath by medical research council (mMRC), test for assessment of chronic obstructive pulmonary disease (CAT) and underwent spirography with bronchodilatation test. Results. It was established, that 10,2 % of patients had the combination of chronic obstructive pulmonary disease with arterial hypertension. Among persons, who were on treatment as to the stable coronary heart disease and had not obstructive disease of respiratory organs in anamnesis, in 26,4 % the chronic obstructive pulmonary disease was diagnosed for the first time.

scholarly journals Dietetic Management of Lipid Profile in an Adult Population from the Italian Region of Abruzzo: Role of Mediterranean Diet

2019 ◽  
pp. 9-21
Author(s):  
Maria Alessandra Gammone ◽  
Stefania Martelli ◽  
Antonella Danese ◽  
Nicolantonio D’Orazio
Keyword(s):  
Mediterranean Diet ◽  
Dairy Products ◽  
Fruits And Vegetables ◽  
Adult Population ◽  
Vascular Diseases ◽  
Metabolic Markers ◽  
The Mediterranean ◽  
Cardio Vascular ◽  
The One

Background: There has long been a lot of debate about the role of nutrition in the pathogenesis of cardio- vascular diseases. Monounsaturated and polyunsaturated fatty acids, especially n-3 PUFAs are the types of fat that favor metabolic markers and represent central components of the Mediterranean diet, which is considered an ideal dietary pattern with great cardio protective effect. Aim: This study aims to assess the influence of Mediterranean diet on lipid metabolism, compared to not-Mediterranean hypocaloric dietary patterns. Materials and Methods: This prospective clinical trial evaluated total cholesterol, LDL, HDL, and triglycerides and their modifications in a group of adults in relation to the two different kinds of diet: on the one hand the typical western dietetic pattern, characterized by higher intakes of red meat, dairy products and refined grains, low consumption of fruits and vegetables (L-diet), and the Mediterranean diet (M-diet).

Sign in / Sign up

Export Citation Format

Share Document