Performance evaluation of different machine learning techniques for prediction of heart disease
| Autor: | Ashok Kumar Dwivedi |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
medicine.medical_specialty
Health awareness Artificial neural network Heart disease business.industry Public health 020206 networking & telecommunications 02 engineering and technology Machine learning computer.software_genre medicine.disease Logistic regression Support vector machine Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Medicine Computational Science and Engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Software |
| Zdroj: | Neural Computing and Applications. 29:685-693 |
| ISSN: | 1433-3058 0941-0643 |
| Popis: | Heart diseases are of notable public health disquiet worldwide. Heart patients are growing speedily owing to deficient health awareness and bad consumption lifestyles. Therefore, it is essential to have a framework that can effectually recognize the prevalence of heart disease in thousands of samples instantaneously. At this juncture, the potential of six machine learning techniques was evaluated for prediction of heart disease. The recital of these methods was assessed on eight diverse classification performance indices. In addition, these methods were assessed on receiver operative characteristic curve. The highest classification accuracy of 85 % was reported using logistic regression with sensitivity and specificity of 89 and 81 %, respectively. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink