Diabetes classification using a redundancy reduction preprocessor

Ribeiro, Áurea Celeste; Barros, Allan Kardec; Santana, Ewaldo Éder Carvalho; Prı́ncipe, José C.

doi:10.1590/1517-3151.0608

Cited by 10 publications

(7 citation statements)

References 36 publications

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“…We would like to emphasize that the accuracy of an algorithm depends on the type of data (dimensionality, origin, and kind); however, SVM is the most successful and widely used classifier. In [46], the work makes emphasis on efficient coding data by decreasing input data redundancy using independent component analysis algorithms (ICA). The results are obtained by testing the algorithm in the Pima Indians Diabetes database, where the SVM algorithm classifies the diabetics with a 98% accuracy rate.…”

Section: Classification With Computational Intelligence Methodsmentioning

confidence: 99%

Deep Learning Classification for Diabetic Foot Thermograms

Cruz-Vega

Hernandez-Contreras

Peregrina-Barreto

et al. 2020

Sensors

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According to the World Health Organization (WHO), Diabetes Mellitus (DM) is one of the most prevalent diseases in the world. It is also associated with a high mortality index. Diabetic foot is one of its main complications, and it comprises the development of plantar ulcers that could result in an amputation. Several works report that thermography is useful to detect changes in the plantar temperature, which could give rise to a higher risk of ulceration. However, the plantar temperature distribution does not follow a particular pattern in diabetic patients, thereby making it difficult to measure the changes. Thus, there is an interest in improving the success of the analysis and classification methods that help to detect abnormal changes in the plantar temperature. All this leads to the use of computer-aided systems, such as those involved in artificial intelligence (AI), which operate with highly complex data structures. This paper compares machine learning-based techniques with Deep Learning (DL) structures. We tested common structures in the mode of transfer learning, including AlexNet and GoogleNet. Moreover, we designed a new DL-structure, which is trained from scratch and is able to reach higher values in terms of accuracy and other quality measures. The main goal of this work is to analyze the use of AI and DL for the classification of diabetic foot thermograms, highlighting their advantages and limitations. To the best of our knowledge, this is the first proposal of DL networks applied to the classification of diabetic foot thermograms. The experiments are conducted over thermograms of DM and control groups. After that, a multi-level classification is performed based on a previously reported thermal change index. The high accuracy obtained shows the usefulness of AI and DL as auxiliary tools to aid during the medical diagnosis.

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Section: Classification With Computational Intelligence Methodsmentioning

confidence: 99%

Deep Learning Classification for Diabetic Foot Thermograms

Cruz-Vega

Hernandez-Contreras

Peregrina-Barreto

et al. 2020

Sensors

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“…Ahmed et al [22] used an improved genetic algorithm an obtained accuracy of 80.4%. Yilmaz et al [23] obtained accuracy of 96.71% using Modified K-Means and SVM and Ribeiro et al [24] measured the accuracy of 97.47% using SVM with efficient coding. The accuracy obtained by our system is of 98.35% using deep neural network for five-fold crossvalidation which is better than the state of the art.…”

Section: Comparative Studymentioning

confidence: 99%

Diabetes Prediction: A Deep Learning Approach

Ayon¹,

Islam²

2019

IJIEEB

131

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Nowadays, Diabetes is one of the most common and severe diseases in Bangladesh as well as all over the world. It is not only harmful to the blood but also causes different kinds of diseases like blindness, renal disease, kidney problem, heart diseases etc. that causes a lot of death per year. So, it badly needs to develop a system that can effectively diagnose the diabetes patients using medical details. We propose a strategy for the diagnosis of diabetes using deep neural network by training its attributes in five-fold and tenfold crossvalidation fashion. The Pima Indian Diabetes (PID) data set is retrieved from the UCI machine learning repository database. The results on PID dataset demonstrate that deep learning approach design an auspicious system for the prediction of diabetes with prediction accuracy of 98.35%, F1 score of 98, and MCC of 97 for five-fold cross-validation. Additionally, accuracy of 97.11%, sensitivity of 96.25%, and specificity of 98.80% are obtained for tenfold cross-validation. The experimental results exhibit that the proposed system provides promising results in case of five-fold cross-validation.

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“…Neuroscience studies suggest that the neural processing stimulates information according to the concept of efficient codding [20]. By applying the aforementioned theory to data regarding faults in transmission lines, the demixture matrix W can be treated as a set of linear filters in the form:…”

Section: Ica Applied To Feature Extraction In a Transmission Linementioning

confidence: 99%