A Machine Learning Based Diabetes Prediction Using Stacking and Stacking With Hyperparameter Tuning
DOI:
https://doi.org/10.26438/ijcse/v10i6.1621Keywords:
Machine Learning, Diabetes, Random Forest, Stacking, Hyperparameter Tuning, LogisticRegressionAbstract
Due to the high blood sugar or blood glucose, the problem of diabetes will occur, and it's also referred to as a metabolic disorder. Long-term high blood glucose levels can result in several heart-related disorders, strokes, renal illness, vision difficulties, dental problems, nerve damage, and other problems. The latest recent information about diabetes worldwide may be found in the IDF Diabetes Atlas, ninth edition 2021.There are 537 million adults facing the problem of diabetes according to the measurement of 2021 year. And we are guessing that there will be total diabetes patients will number 643 million by 2030 and 783 million by 2045. To predict the diabetes, we generally use machine learning algorithms. Here we have executed various machine learning algorithms like K-Nearest Neighbor, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Stacking and Stacking with Hyperparameter Tuning. Each model will have different accuracy in compared to other models. The most accurate result can be achieved by the stacking and stacking with hyperparameter tuning.
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