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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Ibrahim, Zein Al Abidin
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Computer Science & IT 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.
Enhancing aerial image registration: outlier filtering through feature classification Merza, Hayder Mosa; Sbeity, Ihab; Dbouk, Mohamed; Ibrahim, Zein Al Abidin
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.pp1900-1912

Abstract

In the context of feature-based image registration, the crucial task of outlier removal plays a pivotal role in achieving precise registration accuracy. This research introduces an innovative binary classifier founded on an adaptive approach for effectively identifying and eliminating outliers. The methodology begins with the utilization of the scale invariant feature transform (SIFT) to extract features from two images, initially matched using the Euclidian distance metrics. Subsequently, a classification procedure is executed to segregate the feature points into two categories: genuine matches (inliers) and spurious matches (outliers), which is accomplished through the brute-force matcher (BFM) technique. To enhance this process further, a novel classifier rooted in the random forest algorithm is introduced. This classifier is trained and tested using a comprehensive dataset curated for this study. The newly proposed classifier plays a pivotal role in attenuating the influence of outliers, ultimately leading to refined image registration process characterized by enhanced accuracy. The effectiveness of this outlier removal approach is assessed through a meticulous analysis of positional and classification accuracy. Additionally, we offer comparative insights by evaluating the performance of selected algorithm on our dataset.
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. 
Co-Authors Alkhazraji, Ali Alsafi, Fatima Dbouk, Mohamed Dhayni, Houssein Hatoum, Rima Merza, Hayder Mosa Sbeity, Ihab