Classification Techniques Used in Sentiment Analysis & Prediction of Heart Disease using Data Mining Techniques: Review

2019 ◽  
Author(s):  
Rahul ◽  
Himanshu Bansal ◽  
Monika
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Sentiment Analysis ◽  
Classification Techniques ◽  
Data Mining Techniques ◽  
Using Data ◽  
Analysis Prediction

scholarly journals Predicting heart ailment in patients with varying number of features using data mining techniques

2019 ◽  
Vol 8 (1) ◽  
pp. 56
Author(s):  
T R Stella Mary ◽  
Shoney Sebastian
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Random Forest ◽  
Naive Bayes ◽  
Heart Diseases ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Data Mining Techniques ◽  
Using Data ◽  
Almost All

<span>Data mining can be defined as a process of extracting unknown, verifiable and possibly helpful data from information. Among the various ailments, heart ailment is one of the primary reason behind death of individuals around the globe, hence in order to curb this, a detailed analysis is done using Data Mining. Many a times we limit ourselves with minimal attributes that are required to predict a patient with heart disease. By doing so we are missing on a lot of important attributes that are main causes for heart diseases. Hence, this research aims at considering almost all the important features affecting heart disease and performs the analysis step by step with minimal to maximum set of attributes using Data Mining techniques to predict heart ailments. The various classification methods used are Naïve Bayes classifier, Random Forest and Random Tree which are applied on three datasets with different number of attributes but with a common class label. From the analysis performed, it shows that there is a gradual increase in prediction accuracies with the increase in the attributes irrespective of the classifiers used and Naïve Bayes and Random Forest algorithms comparatively outperforms with these sets of data.</span>

Heart disease prediction using data mining techniques

2017 ◽  
Author(s):  
Abhishek Rairikar ◽  
Vedant Kulkarni ◽  
Vikas Sabale ◽  
Harshavardhan Kale ◽  
Anuradha Lamgunde
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Disease Prediction ◽  
Data Mining Techniques ◽  
Using Data

scholarly journals Effective heart disease prediction system using data mining techniques

2018 ◽  
Vol Volume 13 ◽  
pp. 121-124 ◽  
Author(s):  
Poornima Singh ◽  
Sanjay Singh ◽  
Gayatri S Pandi-Jain
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Disease Prediction ◽  
Prediction System ◽  
Data Mining Techniques ◽  
Using Data

scholarly journals Predicting Heart Ailment in Patients with Varying number of Features using Data Mining Techniques

2019 ◽  
Vol 9 (4) ◽  
pp. 2675
Author(s):  
T R Stella Mary ◽  
Shoney Sebastian
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Random Forest ◽  
Naive Bayes ◽  
Heart Diseases ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Data Mining Techniques ◽  
Using Data ◽  
Almost All

<span lang="EN-US">Data mining can be defined as a process of extracting unknown, verifiable and possibly helpful data from information. Among the various ailments, heart ailment is one of the primary reason behind death of individuals around the globe, hence in order to curb this, a detailed analysis is done using Data Mining. Many a times we limit ourselves with minimal attributes that are required to predict a patient with heart disease. By doing so we are missing on a lot of important attributes that are main causes for heart diseases. Hence, this research aims at considering almost all the important features affecting heart disease and performs the analysis step by step with minimal to maximum set of attributes using Data Mining techniques to predict heart ailments. The various classification methods used are Naïve Bayes classifier, Random Forest and Random Tree which are applied on three datasets with different number of attributes but with a common class label. From the analysis performed, it shows that there is a gradual increase in prediction accuracies with the increase in the attributes irrespective of the classifiers used and Naïve Bayes and Random Forest algorithms comparatively outperforms with these sets of data.</span>

Prediction of heart disease using data mining techniques

2016 ◽  
Vol 4 (2-4) ◽  
pp. 193-198 ◽  
Author(s):  
Ritika Chadha ◽  
Shubhankar Mayank
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Data Mining Techniques ◽  
Using Data

Who creates value in a user innovation community? A case study of MyStarbucksIdea.com

2016 ◽  
Vol 40 (2) ◽  
pp. 170-186 ◽  
Author(s):  
Hanjun Lee ◽  
Yongmoo Suh
Keyword(s):  
Data Mining ◽  
Sentiment Analysis ◽  
Open Innovation ◽  
Information Overload ◽  
Classification Models ◽  
Content Type ◽  
Data Mining Techniques ◽  
Using Data ◽  
Basic Features ◽  
Innovation Community

Purpose – Successful open innovation requires that many ideas be posted by a number of users and that the posted ideas be evaluated to find ideas of high quality. As such, successful open innovation community would have inherently information overload problem. The purpose of this paper is to mitigate the information problem by identifying potential idea launchers, so that they can pay attention to their ideas. Design/methodology/approach – This research chose MyStarbucksIdea.com as a target innovation community where users freely share their ideas and comments. We extracted basic features from idea, comment and user information and added further features obtained from sentiment analysis on ideas and comments. Those features are used to develop classification models to identify potential idea launchers, using data mining techniques such as artificial neural network, decision tree and Bayesian network. Findings – The results show that the number of ideas posted and the number of comments posted are the most significant among the features. And most of comment-related sentiment features found to be meaningful, while most of idea-related sentiment features are not in the prediction of idea launchers. In addition, this study show classification rules for the identification of potential idea launchers. Originality/value – This study dealt with information overload problem in an open innovation context. A large volume of textual customer contents from an innovation community were examined and classification models to mitigate the problem were proposed using sentiment analysis and data mining techniques. Experimental results show that the proposed classification models can help the firm identify potential idea launchers for its efficient business innovation.

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