International Journal of BIM and Engineering Science IJBES 2571-1075 10.54216/IJBES https://www.americaspg.com/journals/show/3330 2021 2021 Department: Artificial Intelligence and Machine Learning, Sagar Institute of Research & Technology, Bhopal, India Haritima Haritima Assistant Professor, Computer Engineering department, Noble University, Junagadh, Gujarat, India S. Sakena Benazer Senior Manager, TVR Consulting Services Private Limited, Gajularamaram, Medchal Malkangiri district, Hyderabad - 500055, Telegana, India Tatiraju V. Rajani Kanth Associate Professor, Department of Electrical and Electronics Engineering, KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore, India K. Dhineshkumar 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%. 2024 2024 45 54 10.54216/IJBES.090206 https://www.americaspg.com/articleinfo/22/show/3330