Heart Disease Diagnosis Using Data Mining Techniques

Assari, Ramin; Azimi, Parham; Taghva, Mohammad Reza

doi:10.4172/2162-6359.1000415

Cited by 12 publications

(7 citation statements)

References 11 publications

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“…Authors in [7] authors designed a heart disease prediction system using a group of data classification techniques (Decision Tree, Naïve Bayes). They used data from the "Cleveland Clinic Foundation Heart Disease Dataset" composed of 13 attributes and 303 instances.…”

Section: Literature Reviewmentioning

confidence: 99%

An Adaptive Heart Disease Behavior-Based Prediction System

Emam¹,

Abdo²,

Mahmoud³

2019

ijacsa

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Heart disease prediction is a complex process that is influenced by several factors, including the combination of attributes leading to the possibility of heart disease and availability of these attributes in the database, an accurate selection of these attributes and determining the priority and impact of each of them on the prediction model, and finally selecting the appropriate classification technique to build the model. Most of the previous studies have used some heart disease symptoms as major risk factors to build a heart disease prediction system leading to inaccurate prediction results. The main objective of this study is to build an Adaptive Heart Disease Behavior-Based Prediction System (AHDBP) based on risk factors and behaviors that may lead to heart disease. Different classification algorithms will be deployed to get the most accurate results. 18 attributes were used to build the prediction system. The accuracy of the classification techniques was as follows: Decision Tree 90.34%, Naive Bayes 91.54%, and Neural Networks 94.91%. Neural networks can predict heart disease better than other techniques. The Chi square method has also been applied to determine the difference between the expected and the observed results, and the proposed system proved its accuracy at 86.54%.

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Section: Literature Reviewmentioning

confidence: 99%

An Adaptive Heart Disease Behavior-Based Prediction System

Emam¹,

Abdo²,

Mahmoud³

2019

ijacsa

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“…In 2017, Ramin Assari, Parham Azimi and Mohammad Reza Taghva [12]., made a detailed analysis on how to predict the heart disease at an early stage, identify the risk and treat the patients accordingly. They too proposed a new model based on the obtained rules.…”

Section: Literature Surveymentioning

confidence: 99%

Heart Disease Prediction System Using Linear Regression, Smoreg, And Rep Trees Algorithms

Nanthini¹,

Pandiselvi²

2019

IJITEE

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The broad area of data mining covers most of the fields of research. Its role in medical diagnosis is very motivative to the researchers. It is very easy for the medical practitioners to analyze and treat the disease of the patients at an early stage. The proposed work deals with predicting heart disease of the patients at an early stage. The method was organized in three stages, Data collection, Data preprocessing and Data classification. The dataset for the work was collected from UCI repository. The collected sample was first preprocessed to clean unwanted information from the dataset. Classification operation is then performed on the preprocessed data. Classification is carried out with three different techniques, Linear regression model, SMOreg and REP trees. The results of the three methods were compared based on Root mean squared error and the Absolute error and are tabulated

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“…In circulatory system, blood is transferred through the veins in heart. This muscular system plays a vital role as it transports oxygen, blood and other materials to the various body parts [1]. It might cause serious wellbeing conditions including death if the heart does not work appropriately.…”

Section: Introductionmentioning

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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Heart Disease Diagnosis Using Data Mining Techniques

Cited by 12 publications

References 11 publications

An Adaptive Heart Disease Behavior-Based Prediction System

An Adaptive Heart Disease Behavior-Based Prediction System

Heart Disease Prediction System Using Linear Regression, Smoreg, And Rep Trees Algorithms

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

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