Open research directions for multi label learning

Alluwaici, Mo’ath; Junoh, Ahmad Kadri; Ahmad, Farzana Kabir; Mohsen, Mohamad Farhan Mohamad; Alazaidah, Raed

doi:10.1109/iscaie.2018.8405456

Cited by 10 publications

(2 citation statements)

References 22 publications

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“…Numeric characteristics are variables with continuous values, such as blood pressure or age. Scaling is a strategy to guarantee that the numeric characteristics have a consistent scale, preventing greater values from influencing the models [38]. A prominent scaling approach is standard scaling, which changes the data to have a mean of 0 and a standard deviation of 1.…”

Section: B Dataset Acquisitionmentioning

confidence: 99%

Intelligent Heart Disease Prediction System with Applications in Jordanian Hospitals

Al-Batah,

Alzboon,

Alazaidah

2023

IJACSA

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Heart disease is the leading cause of mortality worldwide. Early identification and prediction can play a crucial role in preventing and treating it. Based on patient data, machine learning techniques may be used to construct cardiac disease prediction models. This work aims to investigate the usage of machine learning models for heart disease prediction utilizing a publicly available dataset. The dataset contains patient information on clinical and demographic characteristics and the presence or absence of cardiac disease. Based on classification performance, many machine learning methods were tested and compared. The findings reveal that machine learning models can predict cardiac disease with accuracy and AUC values. Furthermore, the developed system is used to examine some Jordanian patients, and the predictions of the results are satisfactory. The study's findings might have far-reaching consequences for the early identification and prevention of heart disease, as well as for improving patient outcomes and lowering healthcare expenditures.

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Section: B Dataset Acquisitionmentioning

confidence: 99%

Intelligent Heart Disease Prediction System with Applications in Jordanian Hospitals

Al-Batah,

Alzboon,

Alazaidah

2023

IJACSA

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“…SLC was also divided into two subtypes: binary classification and multiclass classification [ 17 ]. For binary classification, the total number of class labels in the dataset was only two [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Potential of Machine Learning for Predicting Sleep Disorders: A Comprehensive Analysis of Regression and Classification Models

Alazaidah,

Samara,

Aljaidi

et al. 2023

Diagnostics

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Sleep disorder is a disease that can be categorized as both an emotional and physical problem. It imposes several difficulties and problems, such as distress during the day, sleep-wake disorders, anxiety, and several other problems. Hence, the main objective of this research was to utilize the strong capabilities of machine learning in the prediction of sleep disorders. In specific, this research aimed to meet three main objectives. These objectives were to identify the best regression model, the best classification model, and the best learning strategy that highly suited sleep disorder datasets. Considering two related datasets and several evaluation metrics that were related to the tasks of regression and classification, the results revealed the superiority of the MultilayerPerceptron, SMOreg, and KStar regression models compared with the other twenty three regression models. Furthermore, IBK, RandomForest, and RandomizableFilteredClassifier showed superior performance compared with other classification models that belonged to several learning strategies. Finally, the Function learning strategy showed the best predictive performance among the six considered strategies in both datasets and with respect to the most evaluation metrics.

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The significance of capturing the correlations among labels in multi-label classification: An investigative study

Alazaidah,

Alzyoud,

Al-Shanableh

et al. 2023

Proceedings of the 4th International Computer Sciences and Informatics Conference (Icsic 2022)

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Open research directions for multi label learning

Cited by 10 publications

References 22 publications

Intelligent Heart Disease Prediction System with Applications in Jordanian Hospitals

Intelligent Heart Disease Prediction System with Applications in Jordanian Hospitals

Potential of Machine Learning for Predicting Sleep Disorders: A Comprehensive Analysis of Regression and Classification Models

The significance of capturing the correlations among labels in multi-label classification: An investigative study

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