Predicting the Presence of Heart Diseases using Comparative Data Mining and Machine Learning Algorithms
Daniel Ananey-Obiri,Enoch Sarku +1 more
TLDR
This study will use data exploratory and mining techniques to extract hidden patterns using python to seek better performance in predicting heart diseases to reduce the number of tests require for the diagnosis of heart diseases.Abstract:
Heart disease, an example of cardiovascular diseases is the number one notable reason for the death of many people in the world. Of recent, studies have concentrated on using alternative efficient techniques such as data mining and machine learning in the diagnosis of diseases based on certain features of an individual. This study will use data exploratory and mining techniques to extract hidden patterns using python. By this, machine learning algorithms (logistic linear regression, decision tree classifier, Gaussian Naïve Bayes models) will be developed to predict the presence of heart diseases in patients. This will try to seek better performance in predicting heart diseases to reduce the number of tests require for the diagnosis of heart diseases. The k-fold cross validation approach will be used in assessing the resulting models for receiver operating characteristic (ROC) curves (sensitivity against specificity). The dataset was collected from UCI machine learning repository which contains information on patients with heart disease. The dataset has 14 attributes and measured on 303 individuals. General Terms Algorithms, pattern recognition, supervised learning, machine learning, heart disease.read more
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References
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An empirical study of the naive Bayes classifier
TL;DR: This work analyzes the impact of the distribution entropy on the classification error, showing that low-entropy feature distributions yield good performance of naive Bayes and demonstrates that naive Baye works well for certain nearlyfunctional feature dependencies.
Proceedings Article
The Optimality of Naive Bayes.
TL;DR: A sufficient condition for the optimality of naive Bayes is presented and proved, in which the dependence between attributes do exist, and evidence that dependence among attributes may cancel out each other is provided.
Journal ArticleDOI
Prognostic modeling with logistic regression analysis: in search of a sensible strategy in small data sets.
TL;DR: A sensible strategy in small data sets is to apply shrinkage methods in full models that include well-coded predictors that are selected based on external information, such as full models including all available covariables.
Proceedings Article
Decision Tree Analysis on J48 Algorithm for Data Mining
TL;DR: This research is focussed on J48 algorithm which is used to create Univariate Decision Trees and discusses about the idea of multivariate decision tree with process of classify instance by using more than one attribute at each internal node.
Journal ArticleDOI
Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques
TL;DR: This paper has analysed prediction systems for Heart disease using more number of input attributes and shows that out of these three classification models Neural Networks predicts Heart disease with highest accuracy.