Improving Classification Accuracy Using Hybrid Machine Learning Algorithm on Malaria Dataset

Machine Learning Algorithms are integrated into Computer-Aided Design (CAD) methodologies to support medical practitioners in diagnosing patient disorders. This research seeks to enhance the accuracy of classifying malaria-infected erythrocytes (RBCs) through the fusion of Machine Learning Algorithms, resulting in a hybrid classifier. The primary phases involve data preprocessing, segmentation, feature extraction, and RBC classification. This paper introduces a novel hybrid Machine Learning Algorithm, employing two combinations of supervised algorithms. The initial combination encompasses Stochastic Gradient Descent (SGD), Logistic Regression, and Decision Tree, while the second employs Stochastic Gradient Descent (SGD), Xgboost, and Random Forest. The proposed approach, implemented using Python programming, presents an innovative hybrid Machine Learning Algorithm. Through a comparative analysis between individual algorithms and the proposed hybrid algorithm, the paper demonstrates heightened accuracy in classifying malaria data, thus aiding medical practitioners in diagnosis. Among these algorithms, SGD, Logistic Regression, and Decision Tree yield individual accuracy rates of 90.63%, 92.23%, and 93.43% respectively, while the hybrid algorithm achieves 95.64% accuracy on the same dataset. The second hybrid algorithm, combining SGD, Xgboost, and Random Forest, outperforms the initial hybrid version. Individually, these algorithms achieve accuracy rates of 90.63%, 95.86%, and 96.11%. When the proposed hybrid algorithm is applied to the same dataset, accuracy is further enhanced to 96.22%.