A Neural Network with a Learning Vector Quantization Algorithm for Multiclass Classification Using a Modular Approach

2016 ◽  
pp. 171-184 ◽  
Author(s):  
Jonathan Amezcua ◽  
Patricia Melin ◽  
Oscar Castillo
Keyword(s):  
Neural Network ◽  
Vector Quantization ◽  
Multiclass Classification ◽  
Learning Vector Quantization ◽  
Modular Approach

scholarly journals Diagnosa Penyakit Demam Berdarah Dengue (DBD) menggunakan Metode Learning Vector Quantization (LVQ)

2020 ◽  
Vol 4 (3) ◽  
pp. 56
Author(s):  
Firman Tawakal ◽  
Ahmedika Azkiya
Keyword(s):  
Neural Network ◽  
Typhoid Fever ◽  
Vector Quantization ◽  
Dengue Hemorrhagic Fever ◽  
Hemorrhagic Fever ◽  
Learning Vector Quantization ◽  
Mean Square ◽  
Northern Australia ◽  
Neural Network Learning ◽  
Network Learning

Dengue Hemorrhagic Fever is a disease that is carried and transmitted through the mosquito Aedes aegypti and Aedes albopictus which is commonly found in tropical and subtropical regions such as in Indonesia to Northern Australia. in 2013 there are 2.35 million reported cases, which is 37,687 case is heavy cases of DHF. DHF’s symthoms have a similarity with typhoid fever, it often occur wrong handling. Therefore we need a system that is able to diagnose the disease suffered by patients, so that they can recognize whether the patient has DHF or Typhoid. The system will be built using Neural Network Learning Vector Quantization (LVQ) based on the best training results. This research is to diagnose Dengue Hemorrhagic Fever using LVQ with input parameters are hemoglobin, leukocytes, platelets, and heritrocytes. Based on result, the best accuracy is 97,14% with Mean Square Error (MSE) is 0.028571 with 84 train data and 36 test data. Conclution from the research is LVQ method can diagnose DHF Keywords: Dengue Hemorrhagic Fever; Learning Vector Quantization; classification; Neural Network;

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