Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Applied Data Sciences
Altarawneh, Enshirah
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Diagnosing Cardiovascular Diseases using Optimized Machine Learning Algorithms with GridSearchCV Alemerien, Khalid; Alsarayreh, Saleel; Altarawneh, Enshirah
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.280

Abstract

Accurate and timely diseases diagnosis is the most important responsibility in the healthcare industry for protecting the people lives. Many lives can be spared from death if their cases diagnosed accurately and early. One of the dangerous diseases is cardiovascular disease (CVD), is the leading cause of death worldwide, making it one of the hardest conditions to diagnose. Globally, about 17.9 million of people are died because of the cardiovascular disease. In order to assist physicians in this mission, automated solutions based on machine learning and deep learning techniques are introduced. Therefore, machine learning algorithms can diagnose diseases quickly and accurately, which adds a huge value to the medical industry. This gives physicians and patients plenty of time. To address this issue, we utilized several supervised machine learning (ML) techniques with GridSearchCV optimizer. Using the optimization techniques can enhance the performance and accuracy of proposed ML-based models. Therefore, we conducted a comparative analysis study to identify the most efficient classification model using two benchmark real datasets from the online Kaggle repository. Seven popular machine learning techniques were utilized: Decision Tree (DT), Support Vector Machine (SVM), Logistic regression (LR), K-Nearest Neighbor (KNN), Random Forest (RF), XGBoost and Naïve Bayes (NB). The findings revealed that both Random Forest and XGBoost classifiers yields highest results in both of the datasets used in our study in terms of accuracy 95.38% and 98.54%, respectively. The rest of ML algorithms showed less performance in predicting the CVD in terms of accuracy, where DT and RF achieved an accuracy of 98.53% and 98.52%, respectively, on the first dataset. Furthermore, employing the proposed ML-based model in the diagnosing CVD process shows the expected implications for patients and physicians. In addition, it shows the impact of constructing a real comprehensive dataset to enhance the performance of proposed solutions.
A hybrid model for handling the imbalanced multiclass classification problem Alshdaifat, Esra'a; Hussein, Fairouz; Al-shdaifat, Ala'a; Al-Hassan, Malak; Altarawneh, Enshirah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3982-3993

Abstract

Data in many application domains is imbalanced. In machine learning, addressing imbalanced data is crucial to prevent bias towards the dominant class label and ensure that prediction models can learn and predict the minority class proficiently. This paper proposes a hybrid imbalanced classification model (HICD) to address the multiclass imbalanced data problem. The primary idea is to combine effective methods to construct a classification model that can handle multiclass imbalanced data effectively. Four methods are employed: an oversampling method to balance the data, a decomposition method to convert the multiclass problem into a set of binary problems, ensemble classification to integrate base classifiers to improve prediction, and a boosting method to encourage the classifier to pay more attention to misclassified samples. To evaluate the proposed model, seventeen imbalanced datasets from various application domains, featuring different numbers of classes, instances, features, and imbalance ratios, are assessed. The experimental results and statistical significance tests demonstrate that the proposed hybrid model significantly outperforms the standard one-vs-one (OVO) approach and the OVO combined with oversampling technique (SMOTE), both considered state-of-the-art for addressing imbalanced multiclass datasets, in terms of F1-score.
Co-Authors Al-Hassan, Malak Al-shdaifat, Ala'a Alemerien, Khalid Alsarayreh, Saleel Alshdaifat, Esra'a Hussein, Fairouz