A Comparative Study of ML Algorithms for Scenario-agnostic Predictions in Healthcare

The extraction of useful knowledge from collected data has always been the holy grail for enterprises and researchers, supporting efficient decision making, provided service's optimization and profit maximization. However, this task is easier said than done, since it presupposes the application of complex mathematical models/algorithms. Data Analysis has prospered due to the continuous demand to simplify and optimize the knowledge extraction process. Several mechanisms in different domains have been developed, consisting of various techniques to analyze specific data. The need for such mechanisms is even greater in healthcare, since there exist data of different complexity that may provide high-valuable knowledge, if properly analyzed. Considering these challenges, this paper proposes a mechanism for performing Data Analysis in diverse scenarios' healthcare data to extract valuable insights. The mechanism can collect data and apply several Machine Learning algorithms to ensure the best result about the prediction of certain features of the provided data.