Optimizing Healthcare Data Analysis through NLP Machine Learning and Business Intelligence Solutions

All organizations have additional data than ever do and data scientists apply advanced analytic techniques and machine learning ML models on that data to obtain value and make strategic and operational decisions. However, the collected raw data usually comes with various issues like incorrect values and duplicate records. Data preparation refers to the process of transforming such raw and data useful and clean format so that the downstream applications can consume reliably. The objective in this work is to enhance performance dashboards and investigate the use of data analysis techniques to automate data preparation pipeline operations through the use of entity matching, data enhancement, and error detection. The current demand for analytics experts vastly exceeds the supply so here analyse the solution to this problem is to increase the user friendliness of ML framework design to make them more accessible and effective automated machine learning (AutoML) mechanism is better design to solve the problem of expertise by providing fully automated off the shelf solutions for model choice and hyper parameter tuning. It clearly shows that the decisions made in medical facilities are highly data-driven. The results of the study confirm what has been analysed in the literature that medical facilities are moving towards data-based healthcare, together with its benefits.