{"title":"用于2型糖尿病诊断的低复杂度深度学习模型设计。","authors":"Soroush Soltanizadeh, Majid Mobini, Seyedeh Somayeh Naghibi","doi":"10.2174/0115733998307556240819093038","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Recent research demonstrates that diabetes can lead to heart problems, neurological damage, and other illnesses.</p><p><strong>Method: </strong>In this paper, we design a low-complexity Deep Learning (DL)-based model for the diagnosis of type 2 diabetes. In our experiments, we use the publicly available PIMA Indian Diabetes Dataset (PIDD). To obtain a low-complexity and accurate DL architecture, we perform an accuracy-versus-complexity study on several DL models.</p><p><strong>Result: </strong>The results show that the proposed DL structure, including Convolutional Neural Networks and Multi-Layer Perceptron models (i.e., CNN+MLP model) outperforms other models with an accuracy of 93.89%.</p><p><strong>Conclusion: </strong>With these features, the proposed hybrid model can be used in wearable devices and IoT-based health monitoring applications.</p>","PeriodicalId":10825,"journal":{"name":"Current diabetes reviews","volume":" ","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Design of a Low-Complexity Deep Learning Model for Diagnosis of Type 2 Diabetes.\",\"authors\":\"Soroush Soltanizadeh, Majid Mobini, Seyedeh Somayeh Naghibi\",\"doi\":\"10.2174/0115733998307556240819093038\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Recent research demonstrates that diabetes can lead to heart problems, neurological damage, and other illnesses.</p><p><strong>Method: </strong>In this paper, we design a low-complexity Deep Learning (DL)-based model for the diagnosis of type 2 diabetes. In our experiments, we use the publicly available PIMA Indian Diabetes Dataset (PIDD). To obtain a low-complexity and accurate DL architecture, we perform an accuracy-versus-complexity study on several DL models.</p><p><strong>Result: </strong>The results show that the proposed DL structure, including Convolutional Neural Networks and Multi-Layer Perceptron models (i.e., CNN+MLP model) outperforms other models with an accuracy of 93.89%.</p><p><strong>Conclusion: </strong>With these features, the proposed hybrid model can be used in wearable devices and IoT-based health monitoring applications.</p>\",\"PeriodicalId\":10825,\"journal\":{\"name\":\"Current diabetes reviews\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-11-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current diabetes reviews\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2174/0115733998307556240819093038\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current diabetes reviews","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/0115733998307556240819093038","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
Design of a Low-Complexity Deep Learning Model for Diagnosis of Type 2 Diabetes.
Background: Recent research demonstrates that diabetes can lead to heart problems, neurological damage, and other illnesses.
Method: In this paper, we design a low-complexity Deep Learning (DL)-based model for the diagnosis of type 2 diabetes. In our experiments, we use the publicly available PIMA Indian Diabetes Dataset (PIDD). To obtain a low-complexity and accurate DL architecture, we perform an accuracy-versus-complexity study on several DL models.
Result: The results show that the proposed DL structure, including Convolutional Neural Networks and Multi-Layer Perceptron models (i.e., CNN+MLP model) outperforms other models with an accuracy of 93.89%.
Conclusion: With these features, the proposed hybrid model can be used in wearable devices and IoT-based health monitoring applications.
期刊介绍：
Current Diabetes Reviews publishes frontier reviews on all the latest advances on diabetes and its related areas e.g. pharmacology, pathogenesis, complications, epidemiology, clinical care, and therapy. The journal"s aim is to publish the highest quality review articles dedicated to clinical research in the field. The journal is essential reading for all researchers and clinicians who are involved in the field of diabetes.
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