A proposed framework using CAC algorithm to predict systemic lupus erythematosus (SLE)

Gomathi, S.; Narayani, V.

doi:10.1109/startup.2016.7583974

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…There are some methods for evaluating the performance of machine learning models. Analytical research will be supported by the use of different evaluation tools [34]. In this work, we used six basic metrics (accuracy [35], precision [36], recall [37], F-Score [38], AUC [39], and time) to explore the differences between machine learning algorithms.…”

Section: G) Evaluation Metricsmentioning

confidence: 99%

Early Diagnosis for Dengue Disease Prediction Using Efficient Machine Learning Techniques Based on Clinical Data

Abdualgalil

Abraham

Ismael

2022

Journal of Robotics and Control

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Dengue fever is a worldwide issue, especially in Yemen. Although early detection is critical to reducing dengue disease deaths, accurate dengue diagnosis requires a long time due to the numerous clinical examinations. Thus, this issue necessitates the development of a new diagnostic schema. The objective of this work is to develop a diagnostic model for the earlier diagnosis of dengue disease using Efficient Machine Learning Techniques (EMLT). This paper proposed prediction models for dengue disease based on EMLT. Five different efficient machine learning models, including K-Nearest Neighbor (KNN), Gradient Boosting Classifier (GBC), Extra Tree Classifier (ETC), eXtreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LightGBM). All classifiers are trained and tested on the dataset using 10-Fold Cross-Validation and Holdout Cross-Validation approaches. On a test set, all models were evaluated using different metrics: accuracy, F1-sore, Recall, Precision, AUC, and operating time. Based on the findings, the ETC model achieved the highest accuracy in Hold-out and 10-fold cross-validation, with 99.12 % and 99.03 %, respectively. In the Holdout cross-validation approach, we conclude that the best classifier with high accuracy is ETC, which achieved 99.12 %. Finally, the experimental results indicate that classifier performance in holdout cross-validation outperforms 10-fold cross-validation. Accordingly, the proposed dengue prediction system demonstrates its efficacy and effectiveness in assisting doctors in accurately predicting dengue disease.

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Section: G) Evaluation Metricsmentioning

confidence: 99%

Early Diagnosis for Dengue Disease Prediction Using Efficient Machine Learning Techniques Based on Clinical Data

Abdualgalil

Abraham

Ismael

2022

Journal of Robotics and Control

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“…There are several studies in the literature that explore machine learning-based approaches to the Lupus diagnosis [29], [30], but few studies are related to Malar Rash detection [4], [5] on images.…”

Section: Lupus Automatic Detectionmentioning

confidence: 99%

Automatic Detection of Lupus Butterfly Malar Rash Based on Transfer Learning

Souza¹,

Oliveira²,

Casa³

et al. 2020

Anais Do XVI Workshop De Visão Computacional (WVC 2020)

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This work presents an approach to the automatic detection of Butterfly Malar Rash (BMR) in images. BMR is a Lupus symptom characterized by a reddish facial rash that appears symmetrically in the cheeks and the back of the nose. The proposed approach is based on Transfer Learning, a popular approach in Deep Learning that consists in the use of pre-trained models as the starting point for computer vision and natural language processing tasks. To perform the experiments, a database was created with images manually collected from the Instagram social network, searching for images with #butterflyrash. We evaluated the proposed approach with eight Convolutional Neural Networks (CNN) architecture. The experimental results are good results, with a precision of up to 0.957.

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A Butterfly Malar Rash Detection Model for Early Systemic Lupus Erythematosus Diagnosis

Dey,

Mansoor

2023

2023 26th International Conference on Computer and Information Technology (ICCIT)

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A proposed framework using CAC algorithm to predict systemic lupus erythematosus (SLE)

Cited by 3 publications

References 15 publications

Early Diagnosis for Dengue Disease Prediction Using Efficient Machine Learning Techniques Based on Clinical Data

Early Diagnosis for Dengue Disease Prediction Using Efficient Machine Learning Techniques Based on Clinical Data

Automatic Detection of Lupus Butterfly Malar Rash Based on Transfer Learning

A Butterfly Malar Rash Detection Model for Early Systemic Lupus Erythematosus Diagnosis

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