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 Performance Analysis of Rule Based Algorithms Applied to a Cardiovascular Dataset

2013 ◽  
Vol 12 (2) ◽  
pp. 3277-3285
Author(s):  
Dev Mukherji ◽  
Nikita Padalia
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Heart Disease ◽  
Accurate Diagnosis ◽  
Alternative Methods ◽  
Support Vector ◽  
Rule Based ◽  
Prevention And Treatment ◽  
Predictive Methods ◽  
Single Data

Cardiovascular disease is one of the dominant concerns of society, affecting millions of people each year. Early and accurate diagnosis of risk of heart disease is one of major areas of medical research, aimed to aid in its prevention and treatment. Most of the approaches used to predict the occurrence of heart disease use single data mining techniques. However, performances of predictive methods have recently increased upon research into hybrid and alternative methods. This paper analyses the performance of logistic regression, support vector machine, and decision trees along with rule-based hybrids of the three in an attempt to create a more accurate predictive model.

scholarly journals Detection of FAKE NEWS on SOCIAL MEDIA using CLASSIFICATION Data Mining Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 3132-3138
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Media ◽  
Information Exchange ◽  
Learning Algorithm ◽  
Daily Life ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Fake News ◽  
Other Information

In today’s world social media is one of the most important tool for communication that helps people to interact with each other and share their thoughts, knowledge or any other information. Some of the most popular social media websites are Facebook, Twitter, Whatsapp and Wechat etc. Since, it has a large impact on people’s daily life it can be used a source for any fake or misinformation. So it is important that any information presented on social media should be evaluated for its genuineness and originality in terms of the probability of correctness and reliability to trust the information exchange. In this work we have identified the features that can be helpful in predicting whether a given Tweet is Rumor or Information. Two machine learning algorithm are executed using WEKA tool for the classification that is Decision Tree and Support Vector Machine.

Features of the passage of rehabilitation routes by disabled people due to diseases of the circulatory system

2001 ◽  
Vol 82 (2) ◽  
pp. 145-148
Author(s):  
S. V. Chagarova
Keyword(s):  
Cardiovascular Disease ◽  
Cardiovascular Diseases ◽  
Circulatory System ◽  
Full Measure ◽  
Disabled People ◽  
Rehabilitation Programs ◽  
Rehabilitation Measures ◽  
The Individual

The conditions of passing rehabilitation routes by invalids with cardiovascular diseases are studied. The lowest inclusion of invalids by various rehabilitation measures is stated among invalids aged up to 29. They are rarely directed to rehabilitation by physicians of corresponding specialities and are often forced to pay for their treatment in full measure. The data obtained are used to develop the methodical recommendations directed to the optimization of the medicosocial examination and rehabilitation, to increase the efficary of the individual rehabilitation programs made for invalids with cardiovascular disease.

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.

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.

scholarly journals Cardiovascular Disease Prediction System Using Extra Trees Classifier

2019 ◽  
Author(s):  
Rahman Shafique ◽  
Arif Mehmood ◽  
Saleem ullah ◽  
Gyu Sang Choi
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Health Care ◽  
Support Vector Machine ◽  
Prediction Models ◽  
Support Vector ◽  
Prediction System ◽  
Classification Techniques ◽  
Data Mining Techniques ◽  
Tree Classifier

Abstract Heart Disease as cardiovascular disease is the leading cause of death for both men and women. It is the major cause of morbidity and mortality in present society. Therefore, researchers are working to help health care professionals in diagnosing process by using data mining techniques. Although the health care industry is richer in the database this data is not properly mined in order to discover hidden patterns and can able to make decisions based on these patterns. The major goal of this learning refers the extraction of hidden layers by applying numerous data mining techniques that probably give remarkable results in order to ensure the presence of cardiovascular disease among peoples. Data mining classification techniques are used to discover these patterns for research in medical industry. The dataset containing 13 attributes has analyzed for prediction system. The dataset contains some commonly used medical terms like blood pressure, cholesterol level, chest pain and 11 other attributes used to predict cardiovascular disease. The most common and effective classification techniques that are used in mining process are Verdict Tree commonly known as Decision Tree, Extra Trees Classifier, Random Forest, Support Vector Machine, Naive Bays and Logistic Regression has analyzed in this paper. Diagnosing and controlling ratio of deaths from cardiovascular disease Extra classifier trees consider is the best approach. We evaluate these prediction models by using evaluation parameters which are Accuracy, Precision, Recall, and F1-score. As per our experimental results shows accuracy of Extra trees classifier, Logistic Model tree classifier, support vector machine, and naive bays classifiers are 90%, 88%, 87%, 86% respectively. So as per our experiment analysis Extra Tree classifier with highest accuracy considered best approach for predication cardiovascular disease.

scholarly journals Heart Disease Prediction Using Machine Learning

2021 ◽  
pp. 267-276
Author(s):  
Baban. U. Rindhe ◽  
Nikita Ahire ◽  
Rupali Patil ◽  
Shweta Gagare ◽  
Manisha Darade
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Heart Disease ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Whole Body ◽  
Support Vector ◽  
Learning Techniques

Heart-related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need fora reliable, accurate, and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart-related diseases. Heart is the next major organ comparing to the brain which has more priority in the Human body. It pumps the blood and supplies it to all organs of the whole body. Prediction of occurrences of heart diseases in the medical field is significant work. Data analytics is useful for prediction from more information and it helps the medical center to predict various diseases. A huge amount of patient-related data is maintained on monthly basis. The stored data can be useful for the source of predicting the occurrence of future diseases. Some of the data mining and machine learning techniques are used to predict heart diseases, such as Artificial Neural Network (ANN), Random Forest,and Support Vector Machine (SVM).Prediction and diagnosingof heart disease become a challenging factor faced by doctors and hospitals both in India and abroad. To reduce the large scale of deaths from heart diseases, a quick and efficient detection technique is to be discovered. Data mining techniques and machine learning algorithms play a very important role in this area. The researchers accelerating their research works to develop software with thehelp of machine learning algorithms which can help doctors to decide both prediction and diagnosing of heart disease. The main objective of this research project is to predict the heart disease of a patient using machine learning algorithms.

scholarly journals A community based cross sectional study to estimate total cardiovascular risk in rural Punjab

2017 ◽  
Vol 4 (4) ◽  
pp. 1295
Author(s):  
Bibhava Vikramaditya ◽  
Mahesh Satija ◽  
Anurag Chaudhary ◽  
Sarit Sharma ◽  
Sangeeta Girdhar ◽  
...  
Keyword(s):  
Cardiovascular Risk ◽  
Cardiovascular Diseases ◽  
High Risk ◽  
Risk Prediction ◽  
Cross Sectional Study ◽  
World Health ◽  
Sectional Study ◽  
Cvd Risk ◽  
Community Based ◽  
Cross Sectional

Background: Cardiovascular diseases (CVD) are leading cause of non communicable deaths in India. CVD risk prediction charts by World Health Organization/International Society of Hypertension (WHO/ISH) are designed for implementing timely preventive measures. The objective of the study was to assess the prevalence of CVD risk parameters and to estimate total CVD risk among adults aged ≥40 years, using the WHO/ISH risk charts alone and also to assess the effect of the inclusion of additional criteria on CVD risk.Methods: A community based cross sectional study was conducted in fifteen villages of Ludhiana district under rural health training centre of Department of Community Medicine, Dayanand Medical College & Hospital, Ludhiana, Punjab. Desired information was obtained using WHO STEPS survey (STEP wise approach to surveillance) from 324 adults aged ≥40 years. Anthropometric, clinical and laboratory measurements were also performed. WHO/ISH risk prediction chart for South East Asian region (SEAR-D) was used to assess the cardiovascular risk among the subjects.Results: WHO/ISH risk prediction charts identified 16.0% of the subjects with high risk (≥20%) of developing a cardiovascular event. The study population showed higher prevalence of physical inactivity, obesity, abdominal obesity, hypertension and diabetes. Amongst high risk CVD group, maximum prevalence was of hypertension and high perceived stress level. However, the proportion of high CVD risk (≥20%) increased to 33.6% when subjects with blood pressure ≥160/100 mmHg and /or on hypertension medication were added as high risk.Conclusions: A substantial proportion of this community is at high risk of developing cardiovascular diseases.

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.

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