Automated Diagnosis of Anemia Signs using Machine Learning

Anemia, is acommon medical condition distinguished by a shortage of red blood corpusclesposes significant health concerns globally. This should be detected at an early stage for effectual intervention and management. This study presents a multi-faceted approach to anemia detection using machine learning techniques. The dataset employed in this research comprises relevant features associated with anemia, including demographic information and clinical indicators. Initial data exploration involves loading the dataset, exploring its structure, and preprocessing steps such as label encoding to prepare the data for analysis. Machine learning (ML) algorithms, including Logistic Regression(LR), Random Forest(RF), K- Nearest Neighbors(KNN), Support Vector Machine(SVM), Decision Tree, XGBoost, Convolutional Neural Networks, are utilized for predictive modeling and several boosting algorithms such as XGBoost, gradient boosting, Adaboost are also used. These algorithms are chosen for their ability to handle both categorical and numerical data, providing a robust framework for anemia prediction. The evaluation of the models involves cross-validation techniques to ensure their generalizability. Various key performance indicators are employed to assess the accuracy and reliability of the predictive analytics. Moreover, an exploratoryanalysis of data has been undertaken to understand the distribution of anemia cases within the dataset. The findings suggest favorable accuracy and efficacy of the machine learning models in identifying instances of anemia. The system's ability to leverage diverse algorithms enhances its robustness, allowing for reliable predictions across different patient profiles. This research contributes to the development of an effective and scalable anemia detection system, laying the foundation for future work in the integration of additional algorithms and optimization strategies for enhanced accuracy and clinical applicability.