28 فروردین 1403

سجاد احمدیان

مرتبه علمی: استادیار
نشانی: دانشگاه صنعتی کرمانشاه
تحصیلات: دکترای تخصصی / مهندسی کامپیوتر
تلفن: 09188339565
دانشکده: دانشکده فناوری اطلاعات

مشخصات پژوهش

عنوان
An efficient cardiovascular disease detection model based on multilayer perceptron and moth-flame optimization
نوع پژوهش مقاله چاپ شده
کلیدواژه‌ها
artificial neural network, cardiovascular disease, modified moth-flame optimization, multilayer perceptron
پژوهشگران سجاد احمدیان (نفر اول)، سید محمد جعفر جلالی (نفر دوم)، سعید رازیانی (نفر سوم)، عبداله چاله چاله (نفر چهارم)

چکیده

Cardiovascular diseases are the leading cause of death in recent decades, which are increasing due to changes in people's lifestyles. Their treatment has high costs and a long treatment process. Therefore, predicting such diseases can provide care, and prevention services and treatment programs can be very useful to increase the quality of life and reduce the cost of treatment and the risk of death for patients. Various artificial neural network (ANN) techniques and machine learning (ML) algorithms can be used as efficient and reliable methods to automatically analyze and detect the hidden patterns of patient medical records data collected through medical examinations related to cardiovascular diseases. In this paper, the multilayer perceptron (MLP) neural network is employed as a supervised learning approach to detect cardiovascular diseases. Moreover, we propose a modified version of moth-flame optimization algorithm named as MMFO which is used to achieve the optimal values of weights and biases in the MLP to speed-up the training process and provide more accurate predictions. The effectiveness of the proposed method is assessed according to performing extensive experiments on three cardiovascular disease datasets from the UCI repository, and its performance is compared with different state-of-the-art classification approaches. The results reveal that the proposed method performs better than other models in terms of all medical datasets.