Diabetes Diseases Prediction Using Supervised Machine Learning and Neighbourhood Components Analysis

Daanouni, Othmane; Cherradi, Bouchaib; Tmiri, Amal

doi:10.1145/3386723.3387887

Cited by 30 publications

(8 citation statements)

References 12 publications

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“…Some researchers only tried to evaluate the performance of the dataset on a single classifier. For example, Rakshit et al [173] implemented a 2-class DNN after standardization and missing value deletion, Daanouni et al [46] compared four classifiers, namely DT, k-NN, ANN and DNN. In [46], DNN with FS using Neighbourhood Components Analysis (NCA) yielded the best result.…”

Section: Diabetes Prediction Methodsmentioning

confidence: 99%

“…For example, Rakshit et al [173] implemented a 2-class DNN after standardization and missing value deletion, Daanouni et al [46] compared four classifiers, namely DT, k-NN, ANN and DNN. In [46], DNN with FS using Neighbourhood Components Analysis (NCA) yielded the best result. On the contrary, a comparative analysis of different algorithms was performed by authors like Hashi et al [84], and Sisodia and Sisodia [206].…”

Section: Diabetes Prediction Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Malakar¹,

Roy

Das

et al. 2022

Arch Computat Methods Eng

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Disease prediction from diagnostic reports and pathological images using artificial intelligence (AI) and machine learning (ML) is one of the fastest emerging applications in recent days. Researchers are striving to achieve near-perfect results using advanced hardware technologies in amalgamation with AI and ML based approaches. As a result, a large number of AI and ML based methods are found in the literature. A systematic survey describing the state-of-the-art disease prediction methods, specifically chronic disease prediction algorithms, will provide a clear idea about the recent models developed in this field. This will also help the researchers to identify the research gaps present there. To this end, this paper looks over the approaches in the literature designed for predicting chronic diseases like Breast Cancer, Lung Cancer, Leukemia, Heart Disease, Diabetes, Chronic Kidney Disease and Liver Disease. The advantages and disadvantages of various techniques are thoroughly explained. This paper also presents a detailed performance comparison of different methods. Finally, it concludes the survey by highlighting some future research directions in this field that can be addressed through the forthcoming research attempts.

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Section: Diabetes Prediction Methodsmentioning

confidence: 99%

Section: Diabetes Prediction Methodsmentioning

confidence: 99%

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Malakar¹,

Roy

Das

et al. 2022

Arch Computat Methods Eng

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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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“…In reality, the two subsets of AI are employed to health data analysis: the first subset is Machine Learning (ML) and the second is Deep Learning (DL) approaches, including radiography images or computed tomography scans, have been shown to be useful on detection of illness and monitoring [12]- [14], [15]- [17]. As a result, various types of human maladies, like as Parkinson's disease [18]- [21], brain tumor segmentation [22], [23], breast cancer [24], diabetes [25], medical image segmentation [26], and heart disease prediction [27]- [30], atherosclerosis diseases [31], could be identified using such techniques. AI advancements have also contributed in the development of a wide range of other scientific fields [32]- [34], [35]- [39].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Detection on X-Ray Images using a Combining Mechanism of Pre-trained CNNs

Gannour¹,

Hamida²,

Saleh³

et al. 2022

IJACSA

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The COVID-19 infection was sparked by the severe acute respiratory syndrome SARS-CoV-2, as mentioned by the World Health Organization, and originated in Wuhan, Republic of China, eventually extending to every nation worldwide in 2020. This research aims to establish an efficient Medical Diagnosis Support System (MDSS) for recognizing COVID-19 in chest radiography with X-ray data. To build an ever more efficient classifier, this MDSS employs the concatenation mechanism to merge pretrained convolutional neural networks (CNNs) predicated on Transfer Learning (TL) classifiers. In the feature extraction phase, this proposed classifier employs a parallel deep feature extraction approach based on Deep Learning (DL). As a result, this approach increases the accuracy of our proposed model, thus identifying COVID-19 cases with higher accuracy. The suggested concatenation classifier was trained and validated using a Chest Radiography image database with four categories: COVID-19, Normal, Pneumonia, and Tuberculosis during this research. Furthermore, we integrated four separate public X-Ray imaging datasets to construct this dataset. In contrast, our mentioned concatenation classifier achieved 99.66% accuracy and 99.48% sensitivity respectively.

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Diabetes Diseases Prediction Using Supervised Machine Learning and Neighbourhood Components Analysis

Cited by 30 publications

References 12 publications

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

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

COVID-19 Detection on X-Ray Images using a Combining Mechanism of Pre-trained CNNs

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