Automation of Healthcare services using Machine learning
Keywords:
Healthcare, Machine learning, Diagnostics, Accuracy, PerformanceAbstract
Machine learning has changed healthcare and other industries. Healthcare automation using machine learning has improved efficiency, accuracy, and patient outcomes. This groundbreaking technology might simplify diagnosis, treatment planning, and patient care. Machine learning affects diagnostics. Complex algorithms may identify early diseases using imaging scans, pathology reports, and patient data. Machine learning algorithms find patterns and anomalies quicker and more accurately, enhancing prognosis and treatment. Personalized medicine uses machine learning for diagnosis and more. Examining genetic and therapeutic data using algorithms may help physicians tailor treatment. This customized approach enhances medication efficacy and negative effects, improving healthcare. Machine learning is used in predictive analytics in healthcare automation. Previous patient data may help machine learning algorithms forecast sickness patterns, admission rates, and resource allocation.
Purpose: The main purpose of research is to consider issues of performance and accuracy related to automated health care services with machine learning and reducing training and testing time along with accuracy enhancement.
Methods: In order to perform data classification Machine learning methods such as Decision trees, SVMs, neural networks are used. Results: Accuracy in case of proposed work is 99.24% where as it is 98.94% in case of conventional research. Average time consumption of proposed work is below 50 minutes where as in case of conventional work it was above 50 minutes.
Conclusion: It is concluded that proposed work is providing solution with better performance and accuracy
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