Machine Learning-Based Anemia Prediction: A Comparative Analysis of Random Forest, Lasso, and Ridge Algorithms

Abstract

Anemia is one of the most common hematological disorders affecting millions of people worldwide and is characterized by reduced hemoglobin concentration or insufficient healthy red blood cells. Early diagnosis plays a significant role in preventing severe health complications and improving patient care. Machine Learning (ML) has emerged as an effective approach for developing intelligent clinical decision-support systems capable of analyzing medical data with high accuracy. This paper presents a machine learning-based anemia prediction system using Complete Blood Count (CBC) parameters. Three supervised machine learning algorithms, namely Random Forest, Logistic Regression with L1 (Lasso) regularization, and Logistic Regression with L2 (Ridge) regularization, were implemented and evaluated using the same dataset and preprocessing pipeline. The proposed model utilizes five important hematological parameters: Gender, Hemoglobin (Hb), Mean Corpuscular Hemoglobin (MCH), Mean Corpuscular Hemoglobin Concentration (MCHC), and Mean Corpuscular Volume (MCV) for predicting whether a patient is likely to have anemia. A graphical user interface (GUI) developed using Python Tkinter enables users to enter patient blood parameters and receive immediate prediction results. Experimental evaluation was performed using Accuracy, Precision, Recall, and F1-Score. Among the evaluated algorithms, the Random Forest classifier achieved the highest accuracy of 100.00%, outperforming Lasso (99.30%) and Ridge (98.95%). Based on the comparative study, Random Forest was selected as the final deployment model. The proposed system provides a fast, accurate, and user-friendly approach for anemia prediction and demonstrates the effectiveness of ensemble learning techniques in healthcare applications.