• S.Lakshmana Suneel
• M. Tarani.

Anemia is a common health disorder caused by a deficiency of hemoglobin or red blood cells, leading to fatigue, weakness, and serious complications if not detected early. Early diagnosis is essential for effective treatment and prevention. This project presents a machine learning-based system designed to predict anemia using important patient health parameters such as hemoglobin levels, red blood cell count, mean corpuscular volume (MCV), and mean corpuscular hemoglobin (MCH). The dataset is carefully preprocessed to handle missing values and improve overall data quality, which helps enhance the accuracy of the prediction models. Various machine learning algorithms, including Random Forest, Logistic Regression, and Decision Tree, are applied to analyze the clinical data and classify whether an individual is anemic or not. These models are evaluated using performance metrics such as accuracy and precision, with Random Forest providing better results due to its ability to handle complex relationships among features. The system is further integrated into a user-friendly web interface that allows users to input medical data and receive instant predictions. Overall, this project demonstrates the significant role of artificial intelligence in healthcare by enabling early detection of anemia, reducing manual effort, and supporting medical professionals in making accurate and timely decisions

RandomForestClassifier, Logistic Regression, Decision tree, XGBoost classifier, LSTM (Long Short -Term Memory), Support Vector Machines (SVM), MinMaxScaler, LabelEncoder

"Anemia Prediction Using Machine Learning and NLP ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 9, page no.d68-d76, September-2026, Available :http://www.jetir.org/papers/JETIR2609309.pdf

"Anemia Prediction Using Machine Learning and NLP ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 9, page no. ppd68-d76, September-2026, Available at : http://www.jetir.org/papers/JETIR2609309.pdf