Abstract:
Anemia is a common worldwide health problem that can have a major negative impact on a person's quality of life and result in major health issues. Timely intervention depends on early diagnosis and precise Anemia prediction however conventional diagnostic techniques frequently depend on intrusive procedures or subjective interpretation. This thesis investigates the use of machine learning approaches to forecast anemia through the application of explainable artificial intelligence (XAI). In particular, it seeks to create an interpretable model that not only forecasts the risk of anemia but also offers intelligible information about the variables influencing the prediction. A variety of machine learning algorithms are used in this work to optimize the accuracy and reliability of anemia prediction. A robust and flexible approach is ensured by the unique benefits that each of the selected methods—Logistic Regression, Random Forest, Decision Trees, XG-Boost (Extreme Gradient Boosting), Support Vector Machine (SVM), and KNN— brings to the prediction process. These models are trained on a clinically relevant dataset that includes hemoglobin levels, red blood cell counts, and other CBC characteristics that are crucial for detecting anemia. This study uses a large dataset of over 1281 people that contains demographic, clinical, and lifestyle factors associated with anemia risk. Carefully chosen machine learning models are trained and evaluated using the preprocessed data. Predefined metrics like F1 Score, accuracy, precision, and recall are used to assess each algorithm's performance. When compared to other algorithms, Decision Tree performs better than all others in terms of prediction accuracy, with a remarkable 99.03% in our analysis. This implies that decision trees are a better way to forecast anemia. Decision trees' outstanding performance enables the creation of a precise anemia prediction tool. By analyzing readily available patient data, this method can assist medical professionals in preventing anemia and initiating therapy early.
