Enhancing disease classification accuracy through machine learning techniques applied to healthcare data

Abstract

Accurate and timely disease classification is essential for effective healthcare delivery and patient management. Traditional diagnostic methods often struggle with the increasing volume and complexity of healthcare data, limiting their efficiency and accuracy. Machine learning (ML) techniques offer powerful tools to analyze large-scale, high-dimensional healthcare datasets by identifying intricate patterns and correlations that may be overlooked by conventional approaches. This research focuses on enhancing disease classification accuracy by applying various machine learning algorithms to diverse healthcare data sources, including electronic health records, medical imaging, and genomic information. Supervised learning models such as support vector machines, random forests, and deep neural networks are explored to classify diseases across multiple medical domains. Additionally, the study addresses common challenges in healthcare data analytics, such as data preprocessing, feature selection, class imbalance, and model interpretability, which are critical for building reliable ML models. The results demonstrate that tailored machine learning models can significantly improve classification performance, facilitating earlier diagnosis and personalized treatment strategies. Moreover, the integration of domain knowledge and advanced feature engineering contributes to the robustness of the models. This research underscores the potential of machine learning as a transformative approach in healthcare, promoting more accurate disease identification and better patient outcomes. Future work includes exploring explainable AI methods and ensuring ethical considerations in the deployment of ML-based disease classification systems.