A Machine Learning Approach for Accurate Disease Classification in Healthcare Systems
Keywords:
Machine learning, disease classification, healthcare systems, predictive analytics, clinical decision supportAbstract
This study investigates the application of machine learning techniques for achieving accurate disease classification within modern healthcare systems. With the increasing availability of large-scale clinical data from electronic health records, laboratory reports and medical imaging, traditional diagnostic approaches face limitations in handling complex and high-dimensional datasets. The research synthesises secondary empirical evidence to evaluate the performance of various machine learning algorithms, including support vector machines, random forests, gradient boosting and deep neural networks, in classifying diverse diseases. The findings indicate that ensemble and deep learning models demonstrate superior classification accuracy and predictive reliability when supported by effective data preprocessing and optimisation strategies. The study also highlights critical challenges related to model interpretability, data quality and generalisability across heterogeneous patient populations. Overall, the research emphasises the potential of machine learning to enhance diagnostic precision and support data-driven clinical decision-making in intelligent healthcare systems.
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