scholarly journals Heart Disease UCI Machine Learning

2021 ◽  
Vol 5 (02) ◽  
pp. 70-74
Author(s):  
Kasiful Aprianto
Keyword(s):  
Machine Learning ◽  
Heart Disease

Dataset yang digunakan bersumber dari https://archive.ics.uci.edu/ml/datasets/heart+disease

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.

Prediction of Having a Heart Disease Using Machine Learning

2020 ◽  
Author(s):  
Indu Dokare ◽  
Anjali Prithiani ◽  
Hanish Ochani ◽  
Sachin Kanjan ◽  
Dinesh Tarachandani
Keyword(s):  
Machine Learning ◽  
Heart Disease

scholarly journals Data mining techniques with machine learning algorithm to predict patients of heart disease

2021 ◽  
Vol 1088 (1) ◽  
pp. 012035
Author(s):  
Mulyawan ◽  
Agus Bahtiar ◽  
Githera Dwilestari ◽  
Fadhil Muhammad Basysyar ◽  
Nana Suarna
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Heart Disease ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Data Mining Techniques

scholarly journals Identification of Circulating Diagnostic Biomarkers for Coronary Microvascular Disease in Postmenopausal Women Using Machine-Learning Techniques

Metabolites ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 339
Author(s):  
Alicia Arredondo Eve ◽  
Elif Tunc ◽  
Yu-Jeh Liu ◽  
Saumya Agrawal ◽  
Huriye Erbak Yilmaz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Postmenopausal Women ◽  
Diagnostic Test ◽  
Microvascular Disease ◽  
Machine Learning Techniques ◽  
Gas Chromatography Mass Spectrometry ◽  
Circulating Biomarkers ◽  
Diagnostic Biomarkers ◽  
Persistent Symptoms

Coronary microvascular disease (CMD) is a common form of heart disease in postmenopausal women. It is not due to plaque formation but dysfunction of microvessels that feed the heart muscle. The majority of the patients do not receive a proper diagnosis, are discharged prematurely and must go back to the hospital with persistent symptoms. Because of the lack of diagnostic biomarkers, in the current study, we focused on identifying novel circulating biomarkers of CMV that could potentially be used for developing a diagnostic test. We hypothesized that plasma metabolite composition is different for postmenopausal women with no heart disease, CAD, or CMD. A total of 70 postmenopausal women, 26 healthy individuals, 23 individuals with CMD and 21 individuals with CAD were recruited. Their full health screening and tests were completed. Basic cardiac examination, including detailed clinical history, additional disease and prescribed drugs, were noted. Electrocardiograph, transthoracic echocardiography and laboratory analysis were also obtained. Additionally, we performed full metabolite profiling of plasma samples from these individuals using gas chromatography-mass spectrometry (GC–MS) analysis, identified and classified circulating biomarkers using machine learning approaches. Stearic acid and ornithine levels were significantly higher in postmenopausal women with CMD. In contrast, valine levels were higher for women with CAD. Our research identified potential circulating plasma biomarkers of this debilitating heart disease in postmenopausal women, which will have a clinical impact on diagnostic test design in the future.

scholarly journals Machine Learning Based Classification Models for Heart Disease Prediction

2021 ◽  
Vol 1916 (1) ◽  
pp. 012092
Author(s):  
N Karthikeyan ◽  
P Padmanaban ◽  
A Prasanth ◽  
D Ragunath
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Disease Prediction ◽  
Classification Models
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