TY - JOUR
T1 - Classification of Malaria Parasite Plasmodium Falciparum Based on Blood Smear Images Using Support Vector Machine Approach
AU - Chamidah, Nur
AU - Saifudin, Toha
AU - Rulaningtyas, Riries
AU - Mawardi, Adam Anargya
AU - Wardhani, Puspa
AU - Budiantara, I. Nyoman
AU - Siregar, Naufal Ramadhan Al Akhwal
N1 - Publisher Copyright:
© 2025; Los autores.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - Malaria remains a significant global health problem, especially in tropical and subtropical regions. The disease results in a large number of clinical cases and deaths each year, with high-risk groups including infants, toddlers, and pregnant women. Accurate and rapid diagnosis is a key factor in treating this disease. To address this problem, this research aims to develop an automatic system for classifying the malaria parasite Plasmodium Falciparum based on blood smear images. The method used includes image feature selection using Principal Component Analysis (PCA) and the Support Vector Machine (SVM) approach for classification. The results showed that in the image feature selection process, the normal malaria category showed typical characteristics with PC1 and PC2 values that tended to be negative and scattered, while the parasitic malaria category showed greater variability in the PC1 and PC2 components. Furthermore, evaluation of the accuracy of the classification system using SVM with three different kernel types shows promising results. The average accuracy through K-fold cross-validation for the polyinomial, linear, and radial basis function kernels is 96,7 %, 98,9 %, and 94,4 %, respectively. These results highlight the significant potential of utilizing SVM in the classification of the malaria parasite Plasmodium Falciparum based on blood smear images.
AB - Malaria remains a significant global health problem, especially in tropical and subtropical regions. The disease results in a large number of clinical cases and deaths each year, with high-risk groups including infants, toddlers, and pregnant women. Accurate and rapid diagnosis is a key factor in treating this disease. To address this problem, this research aims to develop an automatic system for classifying the malaria parasite Plasmodium Falciparum based on blood smear images. The method used includes image feature selection using Principal Component Analysis (PCA) and the Support Vector Machine (SVM) approach for classification. The results showed that in the image feature selection process, the normal malaria category showed typical characteristics with PC1 and PC2 values that tended to be negative and scattered, while the parasitic malaria category showed greater variability in the PC1 and PC2 components. Furthermore, evaluation of the accuracy of the classification system using SVM with three different kernel types shows promising results. The average accuracy through K-fold cross-validation for the polyinomial, linear, and radial basis function kernels is 96,7 %, 98,9 %, and 94,4 %, respectively. These results highlight the significant potential of utilizing SVM in the classification of the malaria parasite Plasmodium Falciparum based on blood smear images.
KW - Malaria
KW - Parasite Classification
KW - Principal Component Analysis
KW - Support Vector Machine
UR - http://www.scopus.com/inward/record.url?scp=85212976217&partnerID=8YFLogxK
U2 - 10.56294/dm2025568
DO - 10.56294/dm2025568
M3 - Article
AN - SCOPUS:85212976217
SN - 2953-4917
VL - 4
JO - Data and Metadata
JF - Data and Metadata
M1 - 568
ER -