A Review on Effective Disease Classification Using Machine Learning Techniques on Healthcare Data

Abstract

The integration of machine learning (ML) techniques into healthcare has emerged as a transformative approach for disease classification, offering improved diagnostic accuracy, speed, and efficiency. With the exponential growth of healthcare data from electronic health records, medical imaging, laboratory results, and wearable devices, traditional diagnostic methods face limitations in processing and interpreting large volumes of complex data. Machine learning models, particularly supervised learning algorithms such as Logistic Regression, Decision Trees, and Support Vector Machines, are increasingly being used to classify diseases including diabetes, cancer, heart disease, and neurological disorders. These models learn from historical data to identify hidden patterns and relationships between patient attributes and disease outcomes, supporting early detection and personalized treatment plans. Despite the promising capabilities, challenges remain, including data imbalance, lack of standardization, privacy concerns, and the need for model interpretability. Moreover, the successful deployment of ML models requires high-quality datasets and careful validation to ensure clinical relevance and reliability. This review explores the current landscape of ML-based disease classification, evaluates the performance of various algorithms, and highlights recent advancements in the field. It also discusses ongoing challenges and future directions for research. The study contributes to a better understanding of how ML can be leveraged to enhance healthcare outcomes through intelligent, data-driven disease prediction and diagnosis.