Ağaç Tabanlı Makine Öğrenmesi Yöntemleri Kullanılarak ECG Sinyalinden Aritmi Teşhisi

Yakut, Önder

doi:10.38063/ejons.361

Cited by 1 publication

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“…Training takes place on randomly selected subsets and prediction is made on each decision tree. As a result of these estimates, the decision tree and estimate with the highest success rate are selected as the result [15,16].…”

Section: Random Forest Classifiermentioning

confidence: 99%

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Diabetes Prediction Using Colab Notebook Based Machine Learning Methods

Yakut

2023

International Journal of Computational and Experimental Science and Engineering

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Diabetes is getting more and more common around the world. People suffer from diabetes or live at risk associated with this disease. It is necessary to prevent health problems caused by diabetes, to reduce the risk of diabetes and to reduce a load of diabetes on the health system. Therefore, it is important to diagnose and treat diabetic patients early. In this study, Pima Indian Diabetes (PID) database was used to predict diabetes. Random Forest Classifier, Extra Tree Classifier and Gaussian Process Classifier machine learning methods have been used to predict whether individuals have diabetes or not. In this study, the method with the highest prediction accuracy was determined as the Random Forest Classifier. The accuracy of the recommended method was 81.71%. The proposed method was developed to assist clinicians in predicting diabetic patients using diagnostic measurements. The machine learning methods developed in this study were applied using Colab Notebook a Google Cloud Computing service.

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Section: Random Forest Classifiermentioning

confidence: 99%

“…Thus, the diversity between trees increases and the number of splits decreases. Since the calculation cost for the split process decreases, the training time of the model is also shortened [16,17].…”

Section: Extra Trees Classifiermentioning

confidence: 99%

Diabetes Prediction Using Colab Notebook Based Machine Learning Methods

Yakut

2023

International Journal of Computational and Experimental Science and Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

Ağaç Tabanlı Makine Öğrenmesi Yöntemleri Kullanılarak ECG Sinyalinden Aritmi Teşhisi

Cited by 1 publication

References 0 publications

Diabetes Prediction Using Colab Notebook Based Machine Learning Methods

Diabetes Prediction Using Colab Notebook Based Machine Learning Methods

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