Diabetes Detection Using Deep Neural Network

被引:1
|
作者
Mohapatra, Saumendra Kumar [1 ]
Nanda, Susmita [1 ]
Mohanty, Mihir Narayan [1 ]
机构
[1] Siksha O Anusandhan, ITER, Biomed & Speech Proc Lab, Dept Elect & Commun Engn, Bhubaneswar, India
来源
关键词
Diabetes; Deep neural network; Activation function; Classification; Accuracy;
D O I
10.1007/978-981-13-1936-5_25
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Diabetes is rapidly emerging worldwide issue with huge social, health and financial significances. Most of the people in the world suffer from Diabetes respective of new born child to old aged people including male and female. A diabetes patient has high blood sugar and it depend on the production of insulin in the body. Patients suffering from diabetes are treated with special diet and regular exercise. If diabetes is not controlled by the patient there is a chance of higher risk so for this a better treatment is required for this silent killer disease. Here in this paper authors have purposed Deep Neural Network (DNN) for the automatic identification of the disease. The experiment has done with the Pima Indian data set. The classification result has been presented in the result section.
引用
收藏
页码:225 / 231
页数:7
相关论文
共 50 条
  • [31] Weed Detection in Soybean Crop Using Deep Neural Network
    Singh, Vinayak
    Gourisaria, Mahendra Kumar
    Harshvardhan, G. M.
    Choudhury, Tanupriya
    PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY, 2023, 31 (01): : 401 - 423
  • [32] Malware Detection with Deep Neural Network Using Process Behavior
    Tobiyama, Shun
    Yamaguchi, Yukiko
    Shimada, Hajime
    Ikuse, Tomonori
    Yagi, Takeshi
    PROCEEDINGS 2016 IEEE 40TH ANNUAL COMPUTER SOFTWARE AND APPLICATIONS CONFERENCE WORKSHOPS (COMPSAC), VOL 2, 2016, : 577 - 582
  • [33] Detection of Tomato Leaf Miner Using Deep Neural Network
    Jeong, Seongho
    Jeong, Seongkyun
    Bong, Jaehwan
    SENSORS, 2022, 22 (24)
  • [34] Integrated detection and tracking for ADAS using deep neural network
    Liu, Mingjie
    Jin, Cheng-Bin
    Park, Donghun
    Kim, Hakil
    2019 2ND IEEE CONFERENCE ON MULTIMEDIA INFORMATION PROCESSING AND RETRIEVAL (MIPR 2019), 2019, : 71 - 76
  • [35] Plant Disease Detection Using Deep Convolutional Neural Network
    Pandian, J. Arun
    Kumar, V. Dhilip
    Geman, Oana
    Hnatiuc, Mihaela
    Arif, Muhammad
    Kanchanadevi, K.
    APPLIED SCIENCES-BASEL, 2022, 12 (14):
  • [36] ROAD CRACK DETECTION USING DEEP CONVOLUTIONAL NEURAL NETWORK
    Zhang, Lei
    Yang, Fan
    Zhang, Yimin Daniel
    Zhu, Ying Julie
    2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2016, : 3708 - 3712
  • [37] VEHICLE DETECTION IN THERMAL IMAGES USING DEEP NEURAL NETWORK
    Chang, Chin-Wei
    Srinivasan, Kathiravan
    Chen, Yung-Yao
    Cheng, Wen-Huang
    Hua, Kai-Lung
    2018 IEEE INTERNATIONAL CONFERENCE ON VISUAL COMMUNICATIONS AND IMAGE PROCESSING (IEEE VCIP), 2018,
  • [38] Malware Detection Using Gist Features and Deep Neural Network
    Krithika, V
    Vijaya, M. S.
    2020 6TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING AND COMMUNICATION SYSTEMS (ICACCS), 2020, : 800 - 805
  • [39] A deep neural network approach to QRS detection using autoencoders*,**
    Belkadi, Mohamed Amine
    Daamouche, Abdelhamid
    Melgani, Farid
    EXPERT SYSTEMS WITH APPLICATIONS, 2021, 184 (184)
  • [40] Breast Cancer Detection using Deep Convolutional Neural Network
    Mechria, Hana
    Gouider, Mohamed Salah
    Hassine, Khaled
    PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE (ICAART), VOL 2, 2019, : 655 - 660