Improving classification precision for medical decision systems through big data analytics application

Abstract

The rapid evolution of machine learning (ML) and big data analytics has modernized medical decision-making procedure, offering promising path for improving classification precision and ultimately, patient outcomes. This research inspects methodologies for enhancing the classification accuracy of medical decision systems by leveraging ML algorithms and big data analytics procedure. In this study, a broad evaluation of existing literature on ML applications in healthcare and medical decision-making is carried out to discover current challenges and potential areas for improvement. The research explores the integration of diverse data sources, including electronic health records (EHRs), medical imaging, genomic data, and patient-generated data, to build robust predictive models. Moreover, the research emphasizes the importance of interpretability and transparency in ML models for medical decision-making, particularly in critical healthcare settings where the rationale behind predictions is crucial. Techniques for model explainability, such as feature importance analysis and model-agnostic interpretability methods, are explored to enhance trust and adoption of ML-driven decision systems by healthcare professionals. Furthermore, the study investigates advanced ML algorithms such as deep learning, ensemble methods, and feature engineering techniques to extract meaningful patterns from large and complex medical datasets. Through experimentation with real-world medical datasets, the efficacy of these algorithms in improving classification accuracy is evaluated and compared against traditional methods. The result of this research contributes to the advancement of ML-driven medical decision systems by providing insights into strategies for improving classification accuracy, thereby facilitating more exact diagnosis, prognosis, and treatment recommendations. Ultimately, the integration of ML and big data analytics holds immense potential for revolutionizing healthcare delivery and improving patient outcomes.