Analysis of Depth of Entropy and GINI Index Based Decision Trees for Predicting Diabetes

Saud, Arjun Singh; Shakya, Subarna; Neupane, Bindu

doi:10.17010/ijcs/2021/v6/i6/167641

Cited by 7 publications

(3 citation statements)

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“…DT learning algorithm depends on picking up the best-split point on each node. The process of splitting utilizes the concept of Entropy and Information Gain [23], [24] and provides the best data splitting. Information theory inspires entropy, determining the sample values' impurity.…”

Section: B Decision Treementioning

confidence: 99%

Utilizing Various Machine Learning Techniques for Diabetes Mellitus Feature Selection and Classification

Sheta,

Elashmawi,

Al-Qerem

et al. 2024

IJACSA

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Diabetes mellitus is a chronic disease affecting over 38.4 million adults worldwide. Unfortunately, 8.7 million were undiagnosed. Early detection and diagnosis of diabetes can save millions of people's lives. Significant benefits can be achieved if we have the means and tools for the early diagnosis and treatment of diabetes since it can reduce the ratio of cardiovascular disease and mortality rate. It is urgently necessary to explore computational methods and machine learning for possible assistance in the diagnosis of diabetes to support physician decisions. This research utilizes machine learning to diagnose diabetes based on several selected features collected from patients. This research provides a complete process for data handling and pre-processing, feature selection, model development, and evaluation. Among the models tested, our results reveal that Random Forest performs best in accuracy (i.e., 0.945%). This emphasizes Random Forest's efficiency in precisely helping diagnose and reduce the risk of diabetes.

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Section: B Decision Treementioning

confidence: 99%

Utilizing Various Machine Learning Techniques for Diabetes Mellitus Feature Selection and Classification

Sheta,

Elashmawi,

Al-Qerem

et al. 2024

IJACSA

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“…They provide feature importance as part of their output [33]. The importance is calculated based on how much each feature reduces the impurity in the tree [34]. The measure of impurity is either the Gini impurity or the information Gain/entropy [34].…”

Section: Features Selectionmentioning

confidence: 99%

Distributed denial of service attacks classification system using features selection and ensemble techniques

Bagdadi,

Messabih

2024

IJEECS

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Distributed denial-of-service (DDoS) attacks are expanding threat to online services and websites. These attacks overwhelm targets with traffic from multiple sources to exhaust resources and make services unavailable. The frequency of DDoS attacks exhibits an ongoing upward trajectory over time. This persistent escalation highlights the need for effective countermeasures. While machine learning approaches have been extensively investigated for binary classification of DDoS attacks, multi-class classification has received comparatively less examination in the literature despite its greater practical utility. In this paper, we propose an intrusion detection system for detecting and classifying DDoS attacks, based on two main axes: feature selection for selecting the best relevant features and ensemble learning technique for improving performance by combining weak learners. The proposed model has been trained and evaluated on the CICDDoS2019 dataset. Experimental evaluation demonstrates improved performance using a subset of 16 relevant features identified, with a test accuracy of 82.35% attained for discriminating between the 12 classes represented in the dataset. By aggregating attacks sharing common characteristics resulting in 7 classes, the approach achieves surpassing 97% accuracy. Additionally, a binary classification delineating benign and DDoS attacks attain 99.90% accuracy.

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“…This is calculated by comparing the entropy of the dataset before and after splitting it based on the feature of interest. Entropy, on the other hand, is a measure of the degree of impurity or uncertainty in a dataset [31]. It measures the homogeneity of samples within a class.…”

Section: Random Forest -A Supervised Machine Learningmentioning

confidence: 99%

Newborn Cystic Fibrosis Diagnosis Made Accurate and Efficient with Machine Learning to Reduce False Positives in IRT-Trypsinogen Immunoreactive Screening Program

Custódio,

Klausner,

de Almeida

2023

Preprint

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This paper presents a methodology for developing a predictive model using random forests to identify true positive cases of cystic fibrosis in neonatal screening, aiming to improve early detection and care for patients. The current heel prick test used in Brazilian neonatal screening has a high incidence of false positives, leading to unnecessary anxiety and medical interventions for patients and their families. Our methodology, developed using synthetic data and varied model parameters, showed promising results in improving the sensitivity of the model for identifying true positive cases. Our methodology prioritizes sensitivity in the model and uses general indices from the neonatal screening laboratory APAE in São Luís do Maranhão. In conclusion, our approach offers a promising avenue for developing a predictive model for cystic fibrosis in neonatal screening and highlights the potential benefits of improving early detection and care for patients.

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Analysis of Depth of Entropy and GINI Index Based Decision Trees for Predicting Diabetes

Cited by 7 publications

References 0 publications

Utilizing Various Machine Learning Techniques for Diabetes Mellitus Feature Selection and Classification

Utilizing Various Machine Learning Techniques for Diabetes Mellitus Feature Selection and Classification

Distributed denial of service attacks classification system using features selection and ensemble techniques

Newborn Cystic Fibrosis Diagnosis Made Accurate and Efficient with Machine Learning to Reduce False Positives in IRT-Trypsinogen Immunoreactive Screening Program

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