Evaluation of feature scaling for improving the performance of supervised learning methods

Assegie, Tsehay Admassu; Paulraj, Josephin Shermila; Velmurugan, Mani; Devesan, Daya Florance

doi:10.11591/eei.v12i3.5170

Cited by 3 publications

(2 citation statements)

References 17 publications

(18 reference statements)

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“…The task of HF based on the ML model generally mainly includes four steps (Assegie et al, 2023;Austina et al, 2013;Awan et al, 2019;Pudjihartono et al, 2022). After data collection and pre-processing, training and testing data is split.…”

Section: Methodsmentioning

confidence: 99%

Optimal Tree Depth in Decision Tree Classifiers for Predicting Heart Failure Mortality

Assegie,

Elaraby

2023

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The depth of a decision tree (DT) affects the performance of a DT classifier in predicting mortality caused by heart failure (HF). A deeper tree learns complex patterns within the data, theoretically leading to better predictive performance. A very deep tree also leads to overfitting, because the model learns the training data rather than generalize to new and unseen data, resulting in a lower classification performance on test data. Similarly, a shallow tree does not learn much of the complexity within the data, leading to underfitting and a lower performance. The pruning method has been proposed to set a limit on the maximum tree depth or the minimum number of instances required to split a node to reduce the computational complexity. Pruning helps avoid overfitting. However, it does not help find the optimal depth of the tree. To build a better-performing DT classifier, it is crucial to find the optimal tree depth to achieve optimal performance. This study proposed cross-validation to find the optimal tree depth using validation data. In the proposed method, the cross-validated accuracy for training and test data is empirically tested using the HF dataset, which contains 299 observations with 11 features collected from the Kaggle machine learning (ML) data repository. The observed result reveals that tuning the DT depth is significantly important to balance the learning process of the DT because relevant patterns are captured and overfitting is avoided. Although cross-validation techniques prove to be effective in determining the optimal DT depth, this study does not compare different methods to determine the optimal depth, such as grid search, pruning algorithms, or information criteria. This is the limitation of this study.

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Section: Methodsmentioning

confidence: 99%

Optimal Tree Depth in Decision Tree Classifiers for Predicting Heart Failure Mortality

Assegie,

Elaraby

2023

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“…Diabetes has several consequences such as an increased risk of retinopathy, hypertension or high blood pressure, renal damage, and cardiovascular disease [17], [18], [19], [20], [21]. Recently, diabetes is becoming one of the most prevalent diseases affecting numerous people all over the world.…”

Section: Introductionmentioning

confidence: 99%

Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier

Assegie,

Suresh,

Purushothaman

et al. 2023

Journal of Robotics and Control

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Diabetes is one of the quickly spreading chronic diseases causing health complications, such as diabetes retinopathy, kidney failure, and cardiovascular disease. Recently, machine-learning techniques have been widely applied to develop a model for the early prediction of diabetes. Due to its simplicity and generalization capability, K-nearest neighbor (KNN) has been one of the widely employed machine learning techniques for diabetes prediction. Early diabetes prediction has a significant role in managing and preventing complications associated with diabetes, such as retinopathy, kidney failure, and cardiovascular disease. However, the prediction of diabetes in the early stage has remained challenging due to the accuracy and reliability of the KNN model. Thus, gird search hyperparameter optimization is employed to tune the K values of the KNN model to improve its effectiveness in predicting diabetes. The developed hyperparameter-tuned KNN model was tested on the diabetes dataset collected from the UCI machine learning data repository. The dataset contains 768 instances and 8 features. The study applied Min-max scaling to scale the data before fitting it to the KNN model. The result revealed KNN model performance improves when the hyperparameter is tuned. With hyperparameter tuning, the accuracy of KNN improves by 5.29% accuracy achieving 82.5% overall accuracy for predicting diabetes in the early stage. Therefore, the developed KNN model applied to clinical decision-making in predicting diabetes at an early stage. The early identification of diabetes could aid in early intervention, personalized treatment plans, or reducing healthcare costs reducing associated risks such as retinopathy, kidney disease, and cardiovascular disease.

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When to Use Standardization and Normalization: Empirical Evidence From Machine Learning Models and XAI

Mahmud Sujon,

Binti Hassan,

Tusnia Towshi

et al. 2024

IEEE Access

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Evaluation of feature scaling for improving the performance of supervised learning methods

Cited by 3 publications

References 17 publications

Optimal Tree Depth in Decision Tree Classifiers for Predicting Heart Failure Mortality

Optimal Tree Depth in Decision Tree Classifiers for Predicting Heart Failure Mortality

Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier

When to Use Standardization and Normalization: Empirical Evidence From Machine Learning Models and XAI

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