Abstract
In medical science, collecting and classifying data from various diseases is a vital task. The confused and large amounts of data are problems that prevent us from achieving acceptable results. One of the major problems for diabetic patients is a failure to properly diagnose the disease. As a result of this mistake in diagnosis or failure in early diagnosis, the patient may suffer from complications such as blindness, kidney failure, and cutting off the toes. Nowadays, doctors diagnose the disease by relying on their experience and knowledge and performing complex and time-consuming tests. One of the problems with current diabetic, diagnostic methods is the lack of appropriate features to diagnose the disease and consequently the weakness in its diagnosis, especially in its early stages. Since diabetes diagnosis relies on large amounts of data with many parameters, it is necessary to use machine learning methods such as support vector machine (SVM) to predict the complications of diabetes. One of the disadvantages of SVM is its parameter adjustment, which can be accomplished using metaheuristic algorithms such as particle swarm optimization algorithm (PSO), genetic algorithm, or grey wolf optimizer (GWO). In this paper, after preprocessing and preparing the dataset for data mining, we use SVM to predict complications of diabetes based on selected parameters of a patient acquired by laboratory test using improved GWO. We improve the selection process of GWO by employing dynamic adaptive middle filter, a nonlinear filter that assigns appropriate weight to each value based on the data value. Comparison of the final results of the proposed algorithm with classification methods such as a multilayer perceptron neural network, decision tree, simple Bayes, and temporal fuzzy min–max neural network (TFMM-PSO) shows the superiority of the proposed method over the comparable ones.
Highlights
As the twenty-first century progresses, we are witnessing globalization, changes in people’s lifestyles and industrialization, one of the consequences of which is a change in the pattern of diseases [1]
Data samples divided into ten subsets were nine of them used for training, and the remaining one used for testing
We repeated the whole process of choosing test and train data ten times
Summary
As the twenty-first century progresses, we are witnessing globalization, changes in people’s lifestyles and industrialization, one of the consequences of which is a change in the pattern of diseases [1]. Contagious diseases were considered to be the major health problem in third world countries, but the increasing role of noncontagious diseases in mortality, especially in developing countries, is a serious threat. Diabetes is a chronic endocrine disorder characterized by a malfunction in glucose metabolism due to problems with the production or Extended author information available on the last page of the article utilization of insulin hormone. The long-term risks of diabetes are extremely serious for health, such as premature death, blindness, loss of organs if gangrene is not controlled, and impotence. Patients that require insulin treatment and whose disease has begun in childhood, adolescence, or early adulthood are at risk for such problems [1]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
