Towards comparing and using Machine Learning techniques for detecting and predicting Heart Attack and Diseases

Obasi, ThankGod; Shafiq, Omair

doi:10.1109/bigdata47090.2019.9005488

Cited by 53 publications

(15 citation statements)

References 18 publications

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“…Machine learning technique has been widely applied to the classi cation and prediction of various heart diseases such as hypertension and heart failure (21,22). To the best of our knowledge, studies using machine learning to establish the relationship of HRV and LVEF were scarcely conducted in China.…”

Section: Discussionmentioning

confidence: 99%

Prediction of the left ventricular ejection fraction by machine learning algorithms based on heart rate variability parameters in patients with ischemic heart disease

Tian

Han

et al. 2022

Preprint

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Background The left ventricular ejection fraction (LVEF) plays a pivotal role in the diagnosis and prediction of ischemic heart disease (IHD). Current techniques to measure LVEF have led to some complications and are relatively expensive despite the high accuracy. Heart rate variability (HRV) is an alternative for the assessment of cardiac function and its related parameters are easily to be derived from electrocardiography. Objective This study aimed to investigate the corresponding relationship between LVEF and HRV in patients with IHD via prediction models developed by machine learning techniques and compare the accuracy of these models. Methods In this retrospective real-world study, patients with IHD admitted to Jinan 4th People’s hospital between January 2019 and December 2020 were randomly selected and divided into a derivation cohort and a validation cohort in a 7:3 ratio. LVEF were measured by color Doppler echocardiography while HRV-related parameters were derived from a 24h Holter electrocardiography recordings. Patient clinical features and HRV parameters were identified and used in the subsequent modelling process. Logistic regression, naïve Bayes, decision tree, gradient boosting, extreme gradient boosting (XGBoosting) and random forests were respectively applied to estimate the correlation between HRV and LVEF. Model performances were evaluated by the coefficient R2 (the closer the result is to 1, the better) and the accuracy (a precise prediction was defined as the accuracy ≥ 90%). Results A total of 179 patients were included in this study with 125 (mean age 67.03 ± 16.12, male 72 [53.7%]) in the derivation cohort and 54 (mean age 62.51 ± 12.97, male 19 [48.7%]) in the validation cohort. Six variables including sex, age, SDNN, SDANN, rMSSD and pNN50 were found to be correlated with LVEF. Gradient boosting and random forests outperformed other machine learning models, showing the best prediction performance (> 90%) in the prediction of both LVEF percentage (%) and LVEF stratifications (< 40%, 40–50%, > 50%). These six indicators showed different contributions in different algorithms with sex as the least important indicator. Conclusion Gradient boosting and random forests machine learning models based on HRV- and patient characteristics-related parameters provided a new method in predicting LVEF in patients with IHD, which showed a potential in the diagnosis of IHD.

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Section: Discussionmentioning

confidence: 99%

Prediction of the left ventricular ejection fraction by machine learning algorithms based on heart rate variability parameters in patients with ischemic heart disease

Tian

Han

et al. 2022

Preprint

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“…(a) (b) Figure 2: First and Second Schematic diagrams of the model The authors in [28] use random Forest Bayesian Classification and Logistic Regression to predict heart disease in humans or those with risk factors. This model has 18 features and 1990 data points after pre-processing.…”

Section: Table 4: Recent Work On Heart Disease Prediction Authormentioning

confidence: 99%

A Comparison Analysis of Machine Learning Algorithms on Cardiovascular Disease Prediction

Jayasree

Usha

2022

ijfrcsce

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People nowadays are engrossed in their daily routines, concentrating on their jobs and other responsibilities while ignoring their health. Because of their hurried lifestyles and disregard for their health, the number of people becoming ill grows daily. Furthermore, most of the population suffers from a disease such as cardiovascular disease. Cardiovascular disease kills 35% of the world's population, according to W.H.O. A person's life can be saved if a heart disease diagnosis is made early enough. Still, it can also be lost if the diagnosis is constructed incorrectly. Therefore, predicting heart disease will become increasingly relevant in the medical sector. The volume of data collected by the medical industry or hospitals, on the other hand, can be overwhelming at times. Time-series forecasting and processing using machine learning algorithms can help healthcare practitioners become more efficient. In this study, we discussed heart disease and its risk factors and machine learning techniques and compared various heart disease prediction algorithms. Predicting and assessing heart problems is the goal of this research.

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“…They managed to use statistical techniques and Logistic Regression to make relations between attributes. In [5], based on Random Forests Bayesian classification and Logistic Regression, which provides a decision support system for medical professionals to detect and predict heart diseases and heart attacks in humans or individuals using risk factors of heart, in their work, the authors used a big dataset that has 18 features to make the comparison between these three classifiers. Dealing with data before feeding the algorithm for the classification is an important task, and this is the approach proposed in [6], the divided the attributes into ordinal attributes, discrete attributes, and binary attributes to improve classification accuracy, they also used many classifiers such as Decision Tree, Logistic Regression, Bayesian Network.…”

Section: Procedures For Paper Submissionmentioning

confidence: 99%

KNN Algorithm Used for Heart Attack Detection

Bah

2021

FJES

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Machine Learning, a branch of artificial intelligence, has become more accurate than human medical professionals in predicting the incidence of heart attack or death in patients at risk of coronary artery disease. In this paper, we attempt to employ Artificial Intelligence (AI) to predict heart attack. For this purpose, we employed the popular classification technique named the K-Nearest Neighbor (KNN) algorithm to predict the probability of having the Heart Attack (HA). The dataset used is the cardiovascular dataset available publicly on Kaggle, knowing that someone suffering from cardiovascular disease is likely to succumb to a heart attack. In this work, the research was conducted using two approaches. We use the KNN classifier for the first time, aided by using a correlation matrix to select the best features manually and faster computation, and then optimize the parameters with the K-fold cross-validation technique. This improvement led us to have an accuracy of 72.37% on the test set.

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Towards comparing and using Machine Learning techniques for detecting and predicting Heart Attack and Diseases

Cited by 53 publications

References 18 publications

Prediction of the left ventricular ejection fraction by machine learning algorithms based on heart rate variability parameters in patients with ischemic heart disease

Prediction of the left ventricular ejection fraction by machine learning algorithms based on heart rate variability parameters in patients with ischemic heart disease

A Comparison Analysis of Machine Learning Algorithms on Cardiovascular Disease Prediction

KNN Algorithm Used for Heart Attack Detection

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