Leveraging Machine Learning Techniques for Effective Disease Classification in Healthcare Data

Abstract

Effective disease classification plays a crucial role in improving healthcare outcomes, enabling early diagnosis, personalized treatment, and better patient management. This paper explores the application of machine learning (ML) techniques in disease classification using healthcare data. By leveraging algorithms such as XGBoost, Random Forest, and Support Vector Machines (SVM), ML models can process large, complex datasets from sources like medical imaging, clinical records, and genetic information. These algorithms automatically detect patterns within the data, providing healthcare professionals with reliable tools for disease prediction and diagnosis. The study highlights the benefits of using supervised and unsupervised learning techniques, particularly in identifying trends, outliers, and correlations that may not be immediately apparent through traditional methods. Furthermore, the research presents results showing how the accuracy of disease classification models can be improved with the right combination of features and algorithms. However, challenges such as data quality, imbalanced datasets, and the need for interpretability remain significant barriers. The paper also examines emerging techniques such as explainable AI and hybrid models, which aim to improve model transparency and handle complex, multi-modal healthcare data more effectively. The findings demonstrate the potential of ML to transform disease diagnosis and prediction, leading to more efficient and accurate healthcare systems. Future directions in this field include improving model robustness, scalability, and integration into clinical workflows for real-time decision-making.