  With the advent of sophisticated machine learning (ML) techniques and the
promising results they yield, especially in medical applications, where they
have been investigated for different tasks to enhance the decision-making
process. Since visualization is such an effective tool for human comprehension,
memorization, and judgment, we have presented a first-of-its-kind estimation
approach we refer to as Visualized Learning for Machine Learning (VL4ML) that
not only can serve to assist physicians and clinicians in making reasoned
medical decisions, but it also allows to appreciate the uncertainty
visualization, which could raise incertitude in making the appropriate
classification or prediction. For the proof of concept, and to demonstrate the
generalized nature of this visualized estimation approach, five different case
studies are examined for different types of tasks including classification,
regression, and longitudinal prediction. A survey analysis with more than 100
individuals is also conducted to assess users' feedback on this visualized
estimation method. The experiments and the survey demonstrate the practical
merits of the VL4ML that include: (1) appreciating visually clinical/medical
estimations; (2) getting closer to the patients' preferences; (3) improving
doctor-patient communication, and (4) visualizing the uncertainty introduced
through the black box effect of the deployed ML algorithm. All the source codes
are shared via a GitHub repository.
