Effective Prediction of Diabetes Mellitus using Nine different Machine Learning Techniques and their Performances
| Autor: | Shashank Joshi, Anamika Rathod, Vijayendra Gaikwad, Neha Sagar, Sairam Rathod |
|---|---|
| Přispěvatelé: | Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP) |
| Rok vydání: | 2020 |
| Předmět: |
Environmental Engineering
business.industry General Engineering 2249-8958 medicine.disease Machine learning computer.software_genre Computer Science Applications E9626069520/2020©BEIESP Diabetes mellitus medicine Artificial intelligence Diabetes Prediction SVM Random Forest Logical Regression XGB classifier Accuracy Precision Recall F1-Score Nine machine learning techniques Twelve Measures business computer |
| Zdroj: | International Journal of Engineering and Advanced Technology. 9:439-445 |
| ISSN: | 2249-8958 |
| DOI: | 10.35940/ijeat.e9626.069520 |
| Popis: | Diabetes is a disease where the predominant finding is high blood sugar. The high blood sugar may either be because of deficient insulin production (Type 1) or insulin resistance in peripheral tissue cells (Type 2). Many problems occur if diabetes remains untreated and unidentified. It is additional inventor of various varieties of disorders for example: coronary failure, blindness, urinary organ diseases etc. Nine different machine learning techniques are used in this research work for prediction of diabetes. A dataset of diabetic patient’s is taken and nine different machine learning techniques are applied on the dataset. Positive likelihood ratio, Negative likelihood ratio, Positive predictive value, Negative predictive value, Disease prevalence, Specificity, Precision, Recall, F1-Score ,True positive rate, False positive rate of the applied algorithms is discussed and compared. Diabetes is growing at an increasing in the world and it requires continuous monitoring. To check this we use Logical regression, Random forest, Logical regression CV, Support Vector Machine, Artificial Neural Network (ANN), Decision Tree, k-nearest neighbors (KNN), XGB classifier |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|