Effective Analysis and Predictive Model of Stroke Disease using Classification Methods
TLDR
In this work, principle component analysis algorithm is used for reducing the dimensions and it determines the attributes involving more towards the prediction of stroke disease and predicts whether the patient is suffering from stroke disease or not.Abstract:
In today‟s world data mining plays a vital role for prediction of diseases in medical industry. Stroke is a lifethreatning disease that has been ranked third leading cause of death in states and in developing countries. The stroke is a leading cause of serious, long term disability in US. The time taken to recover from stroke disease depends on patients‟ severity. Number of work has been carried out for predicting various diseases by comparing the performance of predictive data mining. Here the classification algorithms like Decision Tree, Naive Bayes and Neural Network is used for predicting the presence of stroke disease with related number of attributes. In our work, principle component analysis algorithm is used for reducing the dimensions and it determines the attributes involving more towards the prediction of stroke disease and predicts whether the patient is suffering from stroke disease or not. General Terms Data mining, Classification algorithm, Stroke disease.read more
Citations
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Journal ArticleDOI
Review of heart disease prediction system using data mining and hybrid intelligent techniques
R. Chitra,V. Seenivasagam +1 more
TL;DR: In this article, the authors summarized the commonly used techniques for heart disease prediction and their complexities are summarized in this paper and observed that Hybrid Intelligent Algorithm improves the accuracy of the prediction system.
Journal ArticleDOI
A Survey of Data Mining Techniques on Risk Prediction: Heart Disease
G. Purusothaman,P. Krishnakumari +1 more
TL;DR: Survey of relevant data mining techniques which are involved in risk prediction of heart disease provides best prediction model as hybrid approach comparing with single model approach.
Journal ArticleDOI
Improvement of heart attack prediction by the feature selection methods
TL;DR: According to the experimental results, the best machine learning algorithm is the support vector machine algorithm with the linear kernel, while the best feature selection algorithms is the reliefF method.
Proceedings ArticleDOI
Stroke prediction using artificial intelligence
TL;DR: This work has the optimum predictive model for the stroke disease with 97.7% accuracy on the Cardiovascular Health Study (CHS) dataset.
References
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Book
Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
BookDOI
To Err Is Human Building a Safer Health System
TL;DR: Boken presenterer en helhetlig strategi for hvordan myndigheter, helsepersonell, industri og forbrukere kan redusere medisinske feil.
Journal ArticleDOI
Original Article: Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules
TL;DR: A weighted fuzzy rule-based clinical decision support system (CDSS) is presented for the diagnosis of heart disease, automatically obtaining knowledge from the patient's clinical data.