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Oussous, Aicha
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Prediction of chronic diseases based on ML packages using spark MLlib Oussous, Aicha; Ez-Zahout, Abderrahmane; Ziti, Soumia
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1121-1129

Abstract

Heart disease, diabetes, and breast cancer pose significant global health challenges, and effectively addressing these chronic diseases necessitates a coordinated international effort. The integration of machine learning and predictive analytics offers promising solutions for tackling these issues. Our study presents a unified model that utilizes the random forest (RF) algorithm and SparkMLlib to predict these three diseases, testing the model on three distinct datasets and evaluating its performance using scientific metrics, including the receiver operating characteristic (ROC) curve, accuracy, precision, recall, and F1-score. Furthermore, we aim to investigate whether variations in medical data and contextual factors impact the results. The findings indicate that while the model shows strong overall performance, its effectiveness may differ for each disease due to factors such as data characteristics, disease-specific features, model behavior, and various biological and medical considerations; understanding these factors is essential for improving model performance and ensuring its appropriate use in clinical environments.
Reviewing chronic ailments: predicting diseases with a multi-symptom approach Oussous, Aicha; Ez-Zahout, Abderrahmane; Ziti, Soumia
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp418-427

Abstract

The integration of machine learning (ML) techniques is now indispensable in healthcare, especially in addressing the challenges posed by chronic illnesses, which present a significant global health concern due to their unpredictable nature. This study compares ML techniques employed in the diagnosis and treatment of chronic conditions such as diabetes, liver disease, thyroid disease, breast cancer, heart disease, Alzheimer’s disease, and others. Two primary criteria guided the selection of diseases under investigation. Firstly, those extensively studied with ML methods, and secondly, those leveraging ML models to resolve issues or yield promising results. The research concludes that in real-time clinical practice, there is no universally proven method for selecting the optimal course of action due to each method’s unique advantages and disadvantages. While a hybrid technique may exhibit slightly slower speed growth, it holds the potential to enhance the accuracy and performance of a model.
Co-Authors Ez-zahout, Abderrahmane Ziti, Soumia