Malaria Disease Prediction Based on Machine Learning

Octave Iradukunda; Haiying Che; Josiane Uwineza; Jean Yves Bayingana; Muhammad S. Bin-Imam; Ibrahim Niyonzima

doi:10.1109/ICSIDP47821.2019.9173011

Malaria Disease Prediction Based on Machine Learning

Octave Iradukunda
, Haiying Che
, Josiane Uwineza
, Jean Yves Bayingana
, Muhammad S. Bin-Imam
, Ibrahim Niyonzima

School of Computer Science and Technology

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

25 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728123455
DOIs	https://doi.org/10.1109/ICSIDP47821.2019.9173011
Publication status	Published - Dec 2019
Event	2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019 - Chongqing, China Duration: 11 Dec 2019 → 13 Dec 2019

Publication series

Name	ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019

Conference

Conference	2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019
Country/Territory	China
City	Chongqing
Period	11/12/19 → 13/12/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

CNN-Keras
DenseNet
Extreme Learning Machine (ELM)
Machine Learning (ML)
Malaria Red Blood Cells
RESNET
VGG16

Access to Document

10.1109/ICSIDP47821.2019.9173011

Cite this

Iradukunda, O., Che, H., Uwineza, J., Bayingana, J. Y., Bin-Imam, M. S., & Niyonzima, I. (2019). Malaria Disease Prediction Based on Machine Learning. In ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019 Article 9173011 (ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSIDP47821.2019.9173011

@inproceedings{709340acc91d41f397a57ab90a150f48,

title = "Malaria Disease Prediction Based on Machine Learning",

abstract = "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.",

keywords = "CNN-Keras, DenseNet, Extreme Learning Machine (ELM), Machine Learning (ML), Malaria Red Blood Cells, RESNET, VGG16",

author = "Octave Iradukunda and Haiying Che and Josiane Uwineza and Bayingana, \{Jean Yves\} and Bin-Imam, \{Muhammad S.\} and Ibrahim Niyonzima",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019 ; Conference date: 11-12-2019 Through 13-12-2019",

year = "2019",

month = dec,

doi = "10.1109/ICSIDP47821.2019.9173011",

language = "English",

series = "ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019",

address = "United States",

}

Iradukunda, O, Che, H, Uwineza, J, Bayingana, JY, Bin-Imam, MS & Niyonzima, I 2019, Malaria Disease Prediction Based on Machine Learning. in ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019., 9173011, ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019, Chongqing, China, 11/12/19. https://doi.org/10.1109/ICSIDP47821.2019.9173011

Malaria Disease Prediction Based on Machine Learning. / Iradukunda, Octave; Che, Haiying; Uwineza, Josiane et al.
ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 9173011 (ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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

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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 -

Iradukunda O, Che H, Uwineza J, Bayingana JY, Bin-Imam MS, Niyonzima I. Malaria Disease Prediction Based on Machine Learning. In ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 9173011. (ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019). doi: 10.1109/ICSIDP47821.2019.9173011

Malaria Disease Prediction Based on Machine Learning

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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