WITHDRAWN: Heart disease early prediction using a novel machine learning method called improved K-means neighbor classifier in python
| Autor: | S. Koteeswaran, Saiyed Faiayaz Waris |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
010302 applied physics
Schedule Heart disease Computer science business.industry k-means clustering 02 engineering and technology Python (programming language) 021001 nanoscience & nanotechnology medicine.disease Machine learning computer.software_genre 01 natural sciences 0103 physical sciences Early prediction Entity–relationship model medicine Artificial intelligence Sensitivity (control systems) 0210 nano-technology business Classifier (UML) computer computer.programming_language |
| Zdroj: | Materials Today: Proceedings. |
| ISSN: | 2214-7853 |
| Popis: | The various existed machine learning classifiers are defined for prediction of early heart disease than the schedule of it, the improved version of K-means neighbour classifier is used that guarantees the more accuracy than actual K-means neighbour classifier and other related classifiers. The terms that influence the heart diseases significantly smoking, food habits, diabetes, blood pressure and other related. In such scenarios, a specific approach or hybrid combination of predicting approaches are required to predict the heart disease very early. In the stages of predicting the heart disease, the classifier is the fourth stage which is significant stage for achieving accuracy, sensitivity, and specificity. When k-means neighbour classification is compared, the ideology titled heart diseases prediction very early using improved k-means neighbour classifier is more efficient in the processing and computing the accuracy. The improved k-means neighbour in the python environment is illustrated with few information sets and produces the output with more accuracy when compared with actual k-means neighbour classifier. The working of the proposed system with architecture and ER Diagrams are defined in the methodology along with pseudo procedure. |
| Databáze: | OpenAIRE |
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