A Survey of Data Mining Techniques on Risk Prediction: Heart Disease

Purusothaman, G.; Krishnakumari, Panchamy

doi:10.17485/ijst/2015/v8i12/58385

Cited by 65 publications

(18 citation statements)

References 9 publications

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“…Manikandan, in his research, developed a system for the diagnosis of myocardial infarction using supervised learning and Bayesian method, with the accuracy of 81 (Manikandan 2017). According to Purusothaman and Krishnakumari research, decision tree methods, k-nearest neighbor11, artificial neural network, support vector machine, and Bayes have 76%, 58%, 85%, 86%, and 69% accuracy in predicting heart disease (Purusothaman and Krishnakumari 2015). In most of these studies data such as ECG, chest pain, shortness of breath, arm pain, addiction, diabetes and heart rate have been used.…”

Section: Intracoronary Thrombus Detected On Angiography or Autopsymentioning

confidence: 99%

A Web-Based Smart Telecare System for Early Diagnosis of Heart Attack

Hosseinpour

Delavar

Baferani

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Acute coronary syndrome which could lead to a heart attack is a common and dangerous disease in many parts of the world. Telecare can accelerate the diagnosis of the disease to provide user information including his/her location in an emergency to a support unit, to reduce the treatment time. Therefore, it is essential to develop a smart telecare system that monitors the status of the user automatically in real-time. This paper has proposed and implemented a web-based smart telecare system. The architecture of this system is based on an architecture for smart care in a smart city. This system receives information from the user and uses linear regression to detect acute coronary syndrome at the initial stage of the disease. This information is descriptive and selected according to the opinion of the cardiologist. Also, in case of diagnosis of acute coronary syndrome, the user's location is determined and sent to a support unit via email. The accuracy of this system is 77.5%. This accuracy is acceptable compared to the same system that predicts myocardial infarction. As the proposed system, unlike the comparator system, does not use electro cardio graphic signals (ECG), it can significantly reduce the time of diagnosis of acute coronary syndrome and its complications through early diagnosis of the disease and its notification. The innovation of this research is the presentation of an intelligent decision maker system that reports the hearth attack to the decision support system via an email message.

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Section: Intracoronary Thrombus Detected On Angiography or Autopsymentioning

confidence: 99%

A Web-Based Smart Telecare System for Early Diagnosis of Heart Attack

Hosseinpour

Delavar

Baferani

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…9 For risk prediction of heart disease, various data mining techniques are applied in the research paper. 10 Analysis of lung cancer is presented by applying different mining techniques by the authors of. 11 Authors of, 12 suggested a method to increase the prediction accuracy of kidney disease.…”

Section: Related Workmentioning

confidence: 99%

Data mining approach for accelerating the classification accuracy of cardiotocography

Potharaju

Sreedevi

Ande

et al. 2019

Clinical Epidemiology and Global Health

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The objective of current study is to increase the classification accuracy of learning algorithms over cardiotocography data by applying preprocessing technique. Due to the diversity of sources, large amount of data is being generated and also has various problems including mislabeled data, missing values, noise, high dimensionality and imbalanced class labels. Method: In this study, we suggested a technique to handle imbalanced data to increase the classification performance of various lazy learners, rule based induction models and tree based models. We used Symmetric Minority Over Sampling Technique (SMOTE) on real dataset to accelerate the performance of various classifiers. We identified that primary dataset is suffering with imbalanced problem, which means the most of the records belong to same class label. Prediction of imbalanced data is biased towards the class with majority instances. To overcome this problem, dataset has to be balanced. Results: As a result of the suggested method the performance of classification algorithms are increased. The obtained result show that majority of classification techniques performed better over balanced dataset when compared with imbalanced dataset. Conclusion: Classification performance over balanced dataset has recorded improved performance than imbalanced dataset after applying the SMOTE.

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“…This tool perform data mining tasks due to the collection of machine learning techniques which are applied directly on datasets. Data preprocessing, classification, clustering, regression, visualization tools, association rules are provided by WEKA tool [4]. This tool is an open source software where ARFF file format is used by WEKA which identify different things using special tags.…”

Section: A Weka Toolmentioning

confidence: 99%

A Review on Intutive Prediction Of Heart Disease Using Data Mining Techniques

Jain

Ahirwar

Pandey

2019

ijcse

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Healthcare evaluates clinical datasets regularly by specialist's learning and action. In the clinical field, computersupported with prediction system is used in the healthcare department. Data mining approach provides innovation and strategy to replace voluminous information into useful data for achieving a decision. By utilizing information mining systems it needs less investment for the forecast of the sickness with more accuracy and precision. This paper evaluates various classifiers and algorithms are used for the expectation of cardiovascular illness.

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A Survey of Data Mining Techniques on Risk Prediction: Heart Disease

Cited by 65 publications

References 9 publications

A Web-Based Smart Telecare System for Early Diagnosis of Heart Attack

A Web-Based Smart Telecare System for Early Diagnosis of Heart Attack

Data mining approach for accelerating the classification accuracy of cardiotocography

A Review on Intutive Prediction Of Heart Disease Using Data Mining Techniques

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