The continuous need for improved medical research cannot be over emphasised considering the threat of different variants of diseases, known and unknown, that continued to plague the World due to several reasons. Artificial Intelligence techniques have been gaining ground, especially different variants of machine learning algorithm for effective and early diagnosis of various diseases. However, there have been several challenges mitigating against taking full advantages of these latest AI technologies, and foremost of these is the “heterogeneous nature of patient healthcare records in formats and location”. Presently researchers mostly focused on one of the specific types of datasets (e.g. clinical data, CT scan output, MRI, notes, etc.) to develop the target predictive solution or any other AI based solutions. This will not allow us take full advantages of the various types of datasets simultaneously, hence, the need to bring in the concepts of ML ensembles that allow different models to be built using different algorithms or different data types or combination of both in unique ways and then those different models can be made to cooperatively and collaboratively unite to achieve the final decision with better performance. Ensemble ML has been established as the best way to take advantage of various AI and ML solutions with several real-life competitions that have demonstrated the superiority of this unique variant of ML paradigm. However, despite the huge potency and power of ensemble techniques in achieving better outcomes always, there is the challenge of running into computational complexity problem as the number of models to be combined increased. The use of superior computing resources to be able to facilitate combining several individual solutions (sometimes hundreds of different solutions needed to be combined) into a single cooperative entity called ensembled solution that will always achieve better outcome is a big buttle-neck.
Therefore, considering the power and advantages presented by HPC, it portends that a careful and systematic coupling of the use of ensemble technique with the superior HPC powers and advantages will go a long way in resolving this computational power challenges of the ensemble ML thereby ushering into the World the unique “HPC-Ensemble ML” as a unique panacea to achieving sustainable and improved medical research and solution deliveries.