Classification of Anaemia Status Using The K-Nearest Neighbor Algorithm

Abstract

Early detection and accurate diagnosis of anemia are crucial for public health management, with conventional methods like complete blood count often being costly and unavailable in remote areas. The use of machine learning techniques, specifically the k-nearest neighbor (KNN) algorithm, shows promise in classifying medical conditions including anemia with competitive accuracy compared to traditional methods. The implementation of KNN not only offers accuracy but also time and cost efficiency, providing reliable results quickly for medical professionals in the field. The algorithm's application involves determining the appropriate k-value for optimal accuracy, calculating distances using Euclidean distance, and voting for class prediction based on nearest neighbors. The analysis showcases the model's efficiency in predicting anemia status with an accuracy of 94.72% and promising precision and recall rates.