2571-1075ISSN (Online)
Volume 9 , Issue 2 , PP: 45-54, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Haritima Mishra 1 * , S. Sakena Benazer 2 , Tatiraju V. Rajani Kanth 3 , K. Dhineshkumar 4
Doi: https://doi.org/10.54216/IJBES.090206
The use of machine learning methods in healthcare has shown encouraging outcomes in terms of better patient care, more efficient use of resources, and streamlined operations. Traditional machine learning methods encounter difficulties when dealing with healthcare data due to its complexity and heterogeneity. Healthcare applications are a good fit for Gradient Boosting Machines (GBMs), which have become a formidable tool for structured data and predictive modelling jobs. Better healthcare system capabilities, including more precise forecasts and well-informed decisions, may be achieved by the integration of GBMs into a hybrid machine learning framework. Using GBMs and Reinforcement Learning (RL), the approach entails creating HealthCareAI, a Hybrid Fusion Learn-Enabled Software Product Line for Healthcare Optimization. Structured healthcare data, including patient information, medical records, and test results, are handled by GBMs. This includes data pre-processing, feature engineering, and GBM model training to forecast outcomes including illness diagnosis, treatment efficacy, and patient prognosis, among others. To optimize treatment planning and resource allocation, the HealthCareAI framework combines GBM models with CNNs for medical image processing and RL. The results show that GBMs in HealthCareAI are effective in boosting prediction accuracy and facilitating healthcare data-driven decision-making. A substantial improvement in predicting accuracy was shown across a range of healthcare jobs once Gradient Boosting Machines (GBMs) were included into HealthCareAI. When compared to more conventional machine learning approaches, GBM models improved illness prediction accuracy by an average of 15%. Even more significant improvements were seen in patient risk stratification, as GBMs successfully identified high-risk patients with an astounding sensitivity of 92% and specificity of 89%.
HealthCareAI , Gradient Boosting Machines (GBMs) , Healthcare optimization , Predictive accuracy , Data-driven decision-making
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