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

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.

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Comparative Analysis of Machine Learning Techniques with Principal Component Analysis on Kidney and Heart Disease

10.1109/icesc51422.2021.9533011 ◽

2021 ◽

Author(s):

Reena Chandra ◽

Manoj Kapil ◽

Avinash Sharma

Keyword(s):

Machine Learning ◽

Principal Component Analysis ◽

Heart Disease ◽

Comparative Analysis ◽

Principal Component ◽

Component Analysis ◽

Machine Learning Techniques ◽

Learning Techniques

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Heart Disease Prediction using Machine Learning Techniques

International Journal of Scientific Research in Science and Technology ◽

10.32628/ijsrst2183218 ◽

2021 ◽

pp. 42-47

Author(s):

Ramesh Ponnala ◽

K. Sai Sowjanya

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Random Forest ◽

Linear Model ◽

Machine Learning Techniques ◽

Disease Prediction ◽

Huge Amount ◽

Healthcare Enterprise ◽

Learning Techniques ◽

Accuracy Level

Prediction of Cardiovascular ailment is an important task inside the vicinity of clinical facts evaluation. Machine learning knowledge of has been proven to be effective in helping in making selections and predicting from the huge amount of facts produced by using the healthcare enterprise. on this paper, we advocate a unique technique that pursuits via finding good sized functions by means of applying ML strategies ensuing in improving the accuracy inside the prediction of heart ailment. The severity of the heart disease is classified primarily based on diverse methods like KNN, choice timber and so on. The prediction version is added with special combos of capabilities and several known classification techniques. We produce a stronger performance level with an accuracy level of a 100% through the prediction version for heart ailment with the Hybrid Random forest area with a linear model (HRFLM).

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Heart Disease Prediction using Machine Learning

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f9780.059120 ◽

2020 ◽

Vol 9 (1) ◽

pp. 700-704

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Machine Learning Techniques ◽

Support Vector ◽

Disease Prediction ◽

Nearest Neighbour ◽

Decision Tree Classifier ◽

Support Vector Classifier ◽

Learning Techniques ◽

Tree Classifier

Deriving the methodologies to detect heart issues at an earlier stage and intimating the patient to improve their health. To resolve this problem, we will use Machine Learning techniques to predict the incidence at an earlier stage. We have a tendency to use sure parameters like age, sex, height, weight, case history, smoking and alcohol consumption and test like pressure ,cholesterol, diabetes, ECG, ECHO for prediction. In machine learning there are many algorithms which will be used to solve this issue. The algorithms include K-Nearest Neighbour, Support vector classifier, decision tree classifier, logistic regression and Random Forest classifier. Using these parameters and algorithms we need to predict whether or not the patient has heart disease or not and recommend the patient to improve his/her health.

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