Predicting diabetes diseases using mixed data and supervised machine learning algorithms

Daanouni, Othmane; Cherradi, Bouchaib; Tmiri, Amal

doi:10.1145/3368756.3369072

Cited by 28 publications

(9 citation statements)

References 16 publications

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“…RF : When confused about taking a decision we often use a technique wherein we take an average of the decisions of many people, then use that average decision as a result [29]. These methods are also called ensemble techniques.…”

Section: Methodsmentioning

confidence: 99%

Data science appositeness in diabetes mellitus diagnosis for healthcare systems of developing nations

et al. 2022

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One of the capacious applications of data science could be its use in bioinformatics. With its proper implementation, chronic diseases like diabetes mellitus, responsible for millions of deaths worldwide, could be diagnosed and predicted with high efficacy. But if not attended, could lead to fatal issues such as kidney failures, heart diseases, and even limb amputation. Diabetic cases have only elevated in numbers in the recent past. The authors use various machine learning, deep learning, and data dimensionality reduction techniques to detect diabetes mellitus. The research is principally conducted on two datasets, first from the Frankfurt Hospital, Germany, second from the University of California, Irvine repository. Models such as support vector machines, Naïve Bayes, and Random Forests were implemented to classify diabetic patients from non‐diabetic ones. Subsequently, after hyperparameter tuning, a comparative study on the results was done and the most prominent model was promoted. This process was repeated for the datasets with reduced dimensionality using linear discriminant analysis and principal component analysis. For the Frankfurt, Germany, dataset, K‐nearest neighbours showed the best accuracy of 98.2%, and the Random Forest classifier for the University of California, Irvine, repository showed 99.2%. With such proficiency, the authors thereby propose a statistical approach for the prediction of diabetes in its early stages. They hope to counter the concern of undiagnosed diabetic cases in developing nations where there is a lack of a basic healthcare system.

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Section: Methodsmentioning

confidence: 99%

Data science appositeness in diabetes mellitus diagnosis for healthcare systems of developing nations

et al. 2022

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“…However, machine learning algorithms and convolutional neural networks (CNNs) models have gained a lot of attention for almost all diseases classification and prediction problematic including breast cancer detection [10], cardio vascular prediction and diagnosis [11], [12], diabetes mellitus prediction [13], [14], etc.…”

Section: Astesj Issn: 2415-6698mentioning

confidence: 99%

Convolutional Neural Network Based Classification of Patients with Pneumonia using X-ray Lung Images

Moujahid¹,

Cherradi²,

Gannour³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Analysis and classification of lung diseases using X-ray images is a primary step in the procedure of pneumonia diagnosis, especially in a critical period as pandemic of COVID-19 that is type of pneumonia. Therefore, an automatic method with high accuracy of classification is needed to perform classification of lung diseases due to the increasing number of cases. Convolutional Neural Networks (CNN) based classification has gained a big popularity over the last few years because of its speed and level of accuracy on the image's classification tasks. Through this article, we propose an implementation a CNNbased classification models using transfer learning technique to perform pneumonia detection and compare the results in order to detect the best model for the task according to certain parameters. As this has become a fast expanding field, there are several models but we will focus on the best outperforming algorithms according to their architecture, length and type of layers and evaluation parameters for the classification tasks. Firstly, we review the existing conventional methods and deep learning architectures used for segmentation in general. Next, we perform a deep performance and analysis based on accuracy and loss function of implemented models. A critical analysis of the results is made to highlight all important issues to improve.

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“…Due to its higher performance in several fields as screening medical face mask [13], image description and a lot of challenges, the exploitation of DL technique in the medical image for classification, detection, and segmentation is highly encouraged [14]. In fact, various human diseases could be detected using such techniques, including COVID-19 [15]- [19], Parkinson's disease [20], breast cancer [21], diabetes diseases [22], medical image segmentation [23], and heart disease prediction [24]- [26]. A vast range of different scientific topics has developed because of advances in AI [27]- [35].…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Detection using MRI Images and Convolutional Neural Network

Lamrani¹,

Cherradi²,

Gannour³

et al. 2022

IJACSA

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A brain tumor is the cause of abnormal growth of cells in the brain. Magnetic resonance imaging (MRI) is the most practical method for detecting brain tumors. Through these MRIs, doctors analyze and identify abnormal tissue growth and can confirm whether the brain is affected by a tumor or not. Today, with the emergence of artificial intelligence techniques, the detection of brain tumors is done by applying the techniques and algorithms of machine learning and deep learning. The advantages of the application of these algorithms are the quick prediction of brain tumors, fewer errors, and greater precision, which help in decision-making and in choosing the most appropriate treatment for patients. In the proposed work, a convolution neural network (CNN) is applied with the aim of detecting the presence of a brain tumor and its performance is analyzed. The main purpose of this article is to adopt the approach of convolutional neural networks as a machine learning technique to perform brain tumor detection and classification. Based on training and testing results, the pre-trained architecture model reaches 96% in precision and classification accuracy rates. For the given dataset, CNN proves to be the better technique for predicting the presence of brain tumors.

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Predicting diabetes diseases using mixed data and supervised machine learning algorithms

Cited by 28 publications

References 16 publications

Data science appositeness in diabetes mellitus diagnosis for healthcare systems of developing nations

Data science appositeness in diabetes mellitus diagnosis for healthcare systems of developing nations

Convolutional Neural Network Based Classification of Patients with Pneumonia using X-ray Lung Images

Brain Tumor Detection using MRI Images and Convolutional Neural Network

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