scholarly journals Diabetes Impacted Cardiovascular Disease Prediction using Machine Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 4376-4378
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Big Data ◽  
Cardiovascular Diseases ◽  
Heart Diseases ◽  
Medical Data ◽  
Structured Data ◽  
Disease Prediction ◽  
Health Communities ◽  
Prediction Systems

Utilizing big data growth in biological and health communities, an accurate analogy of medical data can benefit the detection of diabetes impacting cardiovascular diseases. Using k-Means clustering (kMC) algorithm for structured data of heart disease patients, we narrow down to cardiovascular diseases impacted by diabetes. To our knowledge, none of the previous work focused on predicting heart diseases specifically for diabetes patients. Contrasted to multiple other prediction algorithms, the accuracy of predicting in our proposed algorithm is faster than that of other prediction systems for cardiovascular diseases.

Cardiovascular Disease Prediction Using Data Mining Techniques

2019 ◽  
pp. 246-264
Author(s):  
Kalyani Kadam ◽  
Pooja Vinayak Kamat ◽  
Amita P. Malav
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Big Data ◽  
Cardiovascular Diseases ◽  
Primary Objective ◽  
Disease Prediction ◽  
Data Mining Techniques ◽  
Data Architecture ◽  
Life Threatening ◽  
Using Data

Cardiovascular diseases (CVDs) have turned out to be one of the life-threatening diseases in recent times. The key to effectively managing this is to analyze a huge amount of datasets and effectively mine it to predict and further prevent heart-related diseases. The primary objective of this chapter is to understand and survey various information mining strategies to efficiently determine occurrence of CVDs and also propose a big data architecture for the same. The authors make use of Apache Spark for the implementation.

Cardiovascular Disease Prediction Using Data Mining Techniques

2019 ◽  
pp. 159-179
Author(s):  
Kalyani Kadam ◽  
Pooja Vinayak Kamat ◽  
Amita P. Malav
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Big Data ◽  
Cardiovascular Diseases ◽  
Primary Objective ◽  
Disease Prediction ◽  
Data Mining Techniques ◽  
Data Architecture ◽  
Life Threatening ◽  
Using Data

Cardiovascular diseases (CVDs) have turned out to be one of the life-threatening diseases in recent times. The key to effectively managing this is to analyze a huge amount of datasets and effectively mine it to predict and further prevent heart-related diseases. The primary objective of this chapter is to understand and survey various information mining strategies to efficiently determine occurrence of CVDs and also propose a big data architecture for the same. The authors make use of Apache Spark for the implementation.

Machine Learning Approaches for Cardiovascular Disease Prediction

2021 ◽  
pp. 75-84
Author(s):  
Stuti Pandey ◽  
Abhay Kumar Agarwal
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Human Life ◽  
Medical Science ◽  
Second Primary ◽  
Disease Prediction ◽  
Learning Approaches ◽  
Prediction Systems ◽  
Medical Analysis

In a human body, the heart is the second primary organ after the brain. It causes either a long-term impairment or death of a person if suffering from a cardiovascular disease. In medical science, a proper medical analysis and examination of a cardiovascular disease is very crucial, convincing, and sophisticated task for saving a human life. Data analytics rises because of the absence of sufficient practical tools for exploring the trends and unknown relationships in e-health records. It predicts and achieves information which can ease the diagnosis. This survey examines cardiovascular disease prediction systems developed by different researchers. It also reviews the trend of machine learning approaches used in the past decade with results. Related studies comprise the performance of various classifiers on distinct datasets.

scholarly journals Cardiovascular Disease Prediction using Data Mining Techniques

2017 ◽  
Vol 10 (2) ◽  
pp. 520-528 ◽  
Author(s):  
Mudasir Kirmani
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Cardiovascular Diseases ◽  
High Risk ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Circulatory System ◽  
World Health ◽  
Support Vector ◽  
Predictive Tool

Cardiovascular disease represents various diseases associated with heart, lymphatic system and circulatory system of human body. World Health Organisation (WHO) has reported that cardiovascular diseases have high mortality rate and high risk to cause various disabilities. Most prevalent causes for cardiovascular diseases are behavioural and food habits like tobacco intake, unhealthy diet and obesity, physical inactivity, ageing and addiction to drugs and alcohol are to name few. Factors such as hypertension, diabetes, hyperlipidemia, Stress and other ailments are at high risk to cardiovascular diseases. There have been different techniques to predict the prevalence of cardiovascular diseases in general and heart disease in particular from time to time by implementing variety of algorithms. Detection and management of cardiovascular diseases can be achieved by using computer based predictive tool in data mining. By implementing data mining based techniques there is scope for better and reliable prediction and diagnosis of heart diseases. In this study we studied various available techniques like decision Tree and its variants, Naive Bayes, Neural Networks, Support Vector Machine, Fuzzy Rules, Genetic Algorithms, and Ant Colony Optimization to name few. The observations illustrated that it is difficult to name a single machine learning algorithm for the diagnosis and prognosis of CVD. The study further contemplates on the behaviour, selection and number of factors required for efficient prediction.

scholarly journals Accurate Prediction of Coronary Heart Disease for Patients With Hypertension From Electronic Health Records With Big Data and Machine-Learning Methods: Model Development and Performance Evaluation (Preprint)

2019 ◽  
Author(s):  
Zhenzhen Du ◽  
Yujie Yang ◽  
Jing Zheng ◽  
Qi Li ◽  
Denan Lin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Coronary Heart Disease ◽  
Big Data ◽  
Heart Disease ◽  
Large Population ◽  
High Accuracy ◽  
Learning Methods ◽  
Health Records ◽  
Machine Learning Methods

BACKGROUND Predictions of cardiovascular disease risks based on health records have long attracted broad research interests. Despite extensive efforts, the prediction accuracy has remained unsatisfactory. This raises the question as to whether the data insufficiency, statistical and machine-learning methods, or intrinsic noise have hindered the performance of previous approaches, and how these issues can be alleviated. OBJECTIVE Based on a large population of patients with hypertension in Shenzhen, China, we aimed to establish a high-precision coronary heart disease (CHD) prediction model through big data and machine-learning METHODS Data from a large cohort of 42,676 patients with hypertension, including 20,156 patients with CHD onset, were investigated from electronic health records (EHRs) 1-3 years prior to CHD onset (for CHD-positive cases) or during a disease-free follow-up period of more than 3 years (for CHD-negative cases). The population was divided evenly into independent training and test datasets. Various machine-learning methods were adopted on the training set to achieve high-accuracy prediction models and the results were compared with traditional statistical methods and well-known risk scales. Comparison analyses were performed to investigate the effects of training sample size, factor sets, and modeling approaches on the prediction performance. RESULTS An ensemble method, XGBoost, achieved high accuracy in predicting 3-year CHD onset for the independent test dataset with an area under the receiver operating characteristic curve (AUC) value of 0.943. Comparison analysis showed that nonlinear models (K-nearest neighbor AUC 0.908, random forest AUC 0.938) outperform linear models (logistic regression AUC 0.865) on the same datasets, and machine-learning methods significantly surpassed traditional risk scales or fixed models (eg, Framingham cardiovascular disease risk models). Further analyses revealed that using time-dependent features obtained from multiple records, including both statistical variables and changing-trend variables, helped to improve the performance compared to using only static features. Subpopulation analysis showed that the impact of feature design had a more significant effect on model accuracy than the population size. Marginal effect analysis showed that both traditional and EHR factors exhibited highly nonlinear characteristics with respect to the risk scores. CONCLUSIONS We demonstrated that accurate risk prediction of CHD from EHRs is possible given a sufficiently large population of training data. Sophisticated machine-learning methods played an important role in tackling the heterogeneity and nonlinear nature of disease prediction. Moreover, accumulated EHR data over multiple time points provided additional features that were valuable for risk prediction. Our study highlights the importance of accumulating big data from EHRs for accurate disease predictions.

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.

scholarly journals Is There a Link Between Nutrition, Genetics, and Cardiovascular Disease?

2020 ◽  
Vol 7 (3) ◽  
pp. 33
Author(s):  
Marwan El Ghoch ◽  
Said El Shamieh
Keyword(s):  
Cardiovascular Disease ◽  
Cardiovascular Diseases ◽  
Heart Diseases ◽  
Rheumatic Heart

Cardiovascular diseases (CVDs) are a group of disorders that mainly include coronary, cerebrovascular and rheumatic heart diseases [...]

scholarly journals Ankle-Brachial Index as a Predictor for Cardiovascular Disease in Postmenopausal Women

2019 ◽  
Vol 04 (01) ◽  
pp. 015-019
Author(s):  
Lakshmi Lasya Manchikanti ◽  
Madhuri Taranikanti ◽  
Akhila Dronamraju ◽  
Sudha Bala ◽  
Rohith Kumar Guntuka
Keyword(s):  
Cardiovascular Disease ◽  
Cardiovascular Diseases ◽  
Postmenopausal Women ◽  
Heart Diseases ◽  
Ankle Brachial Index ◽  
Digital Data ◽  
Anthropometric Parameters ◽  
Cross Sectional ◽  
Mortality And Morbidity ◽  
Artery Disease

Abstract Background and Aim Menopausal women are at an increasing risk of cardiovascular diseases due to ovarian failure with estrogen deficiency. Redistribution of fat leading to abdominal obesity is a risk factor for cardiovascular disease. Dyslipidemia is one of the risk factors for peripheral artery disease (PAD) and coronary artery disease (CAD). Prevalence of PAD in women is similar or even higher than men, especially after menopause. ankle-brachial index (ABI) is a gold standard technique to diagnose PAD and acts as an independent prognostic marker to identify patients with high cardiovascular risk. This study aims to compare the ABI between pre- and postmenopausal women and to show that routine use of ABI as a screening tool can be valuable in predicting mortality and morbidity from heart diseases in peri- and postmenopausal women. Material and Methods A cross-sectional study was done on a total of 107 women with no prior medical diseases such as hypertension, diabetes mellitus, cardiovascular diseases, and history of smoking. Fifty pre- and 57 postmenopausal women were included in this study. Anthropometric parameters such as height, weight, and body mass index (BMI) were measured. ABI was calculated by measuring the systolic pressures at posterior tibial artery and brachial artery, as per the protocols using digital data acquisition system. Results BMI in postmenopausal women was significantly higher with p = 0.0023. Systolic and diastolic blood pressures were significantly higher in postmenopausal women (p = 0.000001), and ABI was found to be significantly lower in postmenopausal women particularly on the left side. Conclusion ABI serves as an efficient indicator of PAD. It becomes necessary to understand the progression of PAD as its presence can increase the risk of mortality and morbidity from CAD. Early diagnosis of cardiovascular disease through simple techniques such as ABI measurement would provide scope for early interventional strategies.

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