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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Alkhazraji, Ali
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Towards a disease prediction system: biobert-based medical profile representation Hatoum, Rima; Alkhazraji, Ali; Ibrahim, Zein Al Abidin; Dhayni, Houssein; Sbeity, Ihab
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2314-2322

Abstract

Predicting diseases in advance is crucial in healthcare, allowing for early intervention and potentially saving lives. Machine learning plays a pivotal role in healthcare advancements today. Various studies aim to predict diseases based on prior knowledge. However, a significant challenge lies in representing medical information for machine learning. Patient medical histories are often in an unreadable format, necessitating filtering and conversion into numerical data. Natural language processing (NLP) techniques have made this task more manageable. In this paper, we propose three medical information representations, two of which are based on bidirectional encoder representations from transformers for biomedical text mining (BioBERT), a state-of-the-art text representation technique in the biomedical field. We compare these representations to highlight the powerful advantages of BioBERT-based methods in disease prediction. We evaluate our approach efficiency using the medical information mart for intensive careIII (MIMIC-III) database, containing data from 46,520 patients. Our focus is on predicting coronary artery disease. The results demonstrate the effectiveness of our proposal. In summary, BioBERT, NLP techniques, and the MIMIC-III database are key components in our work, which significantly enhances disease prediction in healthcare.
Machine learning for mental health: predicting transitions from addiction to illness Alkhazraji, Ali; Alsafi, Fatima; Dbouk, Mohamed; Ibrahim, Zein Al Abidin; Sbeity, Ihab
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp385-396

Abstract

The increasing prevalence of infection-causing diseases due to environmental factors and lifestyle choices has strained the healthcare system, necessitating advanced techniques to save lives. Disease prediction plays a crucial role in identifying individuals at risk, enabling early treatment, and benefiting governments and health insurance providers. The collaboration between biomedicine and data science, particularly artificial intelligence and machine learning, has led to significant advancements in this field. However, researchers face challenges related to data availability and quality. Clinical and hospital data, crucial for accurate predictions, are often confidential and not freely accessible. Moreover, healthcare data is predominantly unstructured, requiring extensive cleaning, preprocessing, and labeling. This study aims to predict the likelihood of patients transitioning to mental illness by monitoring addiction conditions and constructing treatment protocols, with the goal of modifying these protocols accordingly. We focus on predicting such transformations to illuminate the underlying factors behind shifts in mental health. To achieve this objective, data from an Iraqi hospital has been collected and analyzed yielding promising results. 
Leveraging Machine Learning for Early Risk Prediction in Cirrhosis Outcome Patients Shakir, Yasir Hussein; Mandhari, Eshaq Aziz Awadh AL; Alkhazraji, Ali
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 3 No. 1: PREDATECS July 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v3i1.2015

Abstract

Millions of individuals worldwide suffer from liver cirrhosis, which is one of the primary causes of mortality. Healthcare professionals may have more opportunities to treat cirrhosis patients effectively if early death prediction is made and it is postulated that death in this cohort would be correlated with laboratory test findings and other relevant diagnoses. In this study five machine learning models, including LR, SVM, XGBoost, AdaBoost and KNN, are implemented and evaluated. The preprocessing steps included feature selection, categorical data encoding, and data balancing using SVMSMOTE. The XGBoost model demonstrated superior performance, achieving 89.55% accuracy, 89.69% precision, 89.55% recall, and an F1-score of 89.59% after balancing. These findings highlight the potential of machine learning models in accurate risk detection in patients with cirrhosis and providing valuable support in clinical decision-making and improving patient treatment.
Co-Authors Alsafi, Fatima Dbouk, Mohamed Dhayni, Houssein Hatoum, Rima Ibrahim, Zein Al Abidin Mandhari, Eshaq Aziz Awadh AL Sbeity, Ihab Shakir, Yasir Hussein