Optimising Malaria Prediction from Cell Images Using Forward Selection and Support Vector Machine Classifier

Abstract

Malaria is a significant health concern, primarily affecting tropical and subtropical regions. Traditional
diagnostic methods for malaria detection, such as microscopic blood smear analysis of cell images, are time
consuming, dependent on trained specialists, and prone to variability. Timely and accurate malaria detection is
crucial for prompt treatment and preventing severe complications. Therefore, this study developed a machine
learning (ML)-based model that could accurately predict malaria by analysing microscopic cell images,
enabling efficient and reliable diagnosis to support timely treatment decisions. Using the Kaggle malaria
dataset comprising 26,159 blood smear images, this study uniquely integrates forward feature selection and
Support Vector Machines (SVM) to enhance malaria prediction accuracy. Unlike existing works, it addresses
gaps in transparency and reproducibility in feature selection methods used for high-dimensional medical image
datasets. Forward selection was employed to optimise and select relevant features for the model, reducing
computational complexity and enhancing its performance. The SVM model achieved an accuracy of 97.1%,
recall of 97.4%, precision of 96.8%, F1-score of 96.9%, and an AUC score of 97.4%. These findings highlight
the potential of ML in automating malaria detection and demonstrate the practical advantage of combining
feature selection with high-performing classifier to optimise diagnostic workflows, especially in resource
limited settings.