TY - GEN
T1 - Malaria Disease Prediction Based on Machine Learning
AU - Iradukunda, Octave
AU - Che, Haiying
AU - Uwineza, Josiane
AU - Bayingana, Jean Yves
AU - Bin-Imam, Muhammad S.
AU - Niyonzima, Ibrahim
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Malaria detection is a stressful job for most doctors and it requires experiences and expertise. The machine learing (ML) method can be used to releave this issue. This paper try to find suitable model to help detect malaria with accuracy. The used dataset was released by National Institute of Health in USA and contained a total number of 27,560 red blood cell (RBC) images with equivalent instances of parasitized and uninfected RBCs images. A single hidden layer feedforward neural networks methodology namely Extreme Learning Machine (ELM) model was applied to classify and predict whether a patient has been affected by malaria or not. ELM has been compared with other machine learning techniques like SVM, KNN, CART, RF, CNN, VGG16, RESNET, and DENSENET, and it has outperformed all the other with 99% of accuracy, 28seconds cost time, 0.0095 Misclassification Error, and 98% precision which showed the effectiveness of ELM in the application of malaria cell detection scenario and it can also be referred by other researchers in the related field.
AB - Malaria detection is a stressful job for most doctors and it requires experiences and expertise. The machine learing (ML) method can be used to releave this issue. This paper try to find suitable model to help detect malaria with accuracy. The used dataset was released by National Institute of Health in USA and contained a total number of 27,560 red blood cell (RBC) images with equivalent instances of parasitized and uninfected RBCs images. A single hidden layer feedforward neural networks methodology namely Extreme Learning Machine (ELM) model was applied to classify and predict whether a patient has been affected by malaria or not. ELM has been compared with other machine learning techniques like SVM, KNN, CART, RF, CNN, VGG16, RESNET, and DENSENET, and it has outperformed all the other with 99% of accuracy, 28seconds cost time, 0.0095 Misclassification Error, and 98% precision which showed the effectiveness of ELM in the application of malaria cell detection scenario and it can also be referred by other researchers in the related field.
KW - CNN-Keras
KW - DenseNet
KW - Extreme Learning Machine (ELM)
KW - Machine Learning (ML)
KW - Malaria Red Blood Cells
KW - RESNET
KW - VGG16
UR - http://www.scopus.com/inward/record.url?scp=85091918043&partnerID=8YFLogxK
U2 - 10.1109/ICSIDP47821.2019.9173011
DO - 10.1109/ICSIDP47821.2019.9173011
M3 - Conference contribution
AN - SCOPUS:85091918043
T3 - ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019
BT - ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019
Y2 - 11 December 2019 through 13 December 2019
ER -
