An Analysis Model For Machine Learning Using Support Vector Machine For The Prediction Of Diabetic Retinopathy

Remigio Ismael Hurtado Ortiz, Janneth Eufemia Matute Argudo, Juan Alberto Boni Lema

Research output: Contribution to conferencePaper

Abstract

Diabetic Retinopathy is a public health disease worldwide, which shows that around one percent of the population suffers from this disease. Likewise, another one percent of patients in the population suffer from this disease, but it is not diagnosed. It is estimated that, within three years, millions of people will suffer from this disease. This will increase the percentage of vascular, ophthalmological and neurological complications, which will translate into premature deaths and deterioration in the quality of life of patients. That is why we face a great challenge, which is to predict and detect the signs of diabetic retinopathy at an early stage. For this reason, this paper presents a Machine Learning model focused on the optimization of a classification method using support vector machines for the early prediction of Diabetic Retinopathy. The optimization of the support vector machine consists of adjusting parameters such as: separation margin penalty between support vectors, separation kernel, among others. This method has been trained using an image dataset called Messidor. In this way, the extraction and preprocessing of the data is carried out to carry out a descriptive analysis and obtain the most relevant variables through supervised learning. In this sense, we can see that the most outstanding variables for the risk of diabetic retinopathy are type 1 diabetes and type 2 diabetes. For the evaluation of the proposed method we have used quality measures such as: MAE, MSE, RSME, but the most important are Accuracy, Precision, Recall and F1 for the optimization of classification problems. Therefore, to show the efficacy and effectiveness of the proposed method, we have used a public database, which has allowed us to accurately predict the signs of diabetic retinopathy. Our method has been compared with other relevant methods in classification problems, such as neural networks and genetic algorithms. The support vector machine has proven to be the best for its accuracy. In the state of the art, the works related to Diabetic Retinopathy are presented, as well as the outstanding works with respect to Machine Learning and especially the most outstanding works in Support Vector Machines. We have described the main parameters of the method and also the general process of the algorithm with the description of each step of the analysis model. We have included the values of hyper parameters experienced in the compared methods. In this way we present the best values of the parameters that have generated the best results. Finally, the most relevant results and the corresponding analysis are presented, where the results of the comparison made with the methods of Neural Networks, SVM and Genetic Algorithm will be evidenced. This study gives way to future research related to diabetic retinopathy with the aim of conjecturing the information and thus seeking a better solution.
Translated title of the contributionUn modelo de análisis para el aprendizaje automático utilizando una máquina de vectores de soporte para la predicción de la retinopatía diabética
Original languageEnglish (US)
DOIs
StatePublished - 21 Jul 2022
EventAHFE 2022 Conference on Applied Human Factors and Ergonomics International - US
Duration: 24 Jul 202228 Jul 2022
https://2022.ahfe.org/

Conference

ConferenceAHFE 2022 Conference on Applied Human Factors and Ergonomics International
Period24/07/2228/07/22
Internet address

Keywords

  • Artificial intelligence
  • Support vector machine
  • Machine learning
  • Neural networks

CACES Knowledge Areas

  • 116A Computer Science

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