Machine learning has been dramatically advanced over several decades, from theory context to a general business and technology implementation. Especially in healthcare research, it is obvious to perceive the scrutinizing implementation of machine learning to warranty the rewarded benefits in early disease detection and service recommendation. Many practitioners and researchers have eventually recognized no absolute winner approach to all kinds of data. Even when implicit, the learning algorithms rely on learning parameters, hyperparameters tuning to find the best values for these coefficients that optimize a particular evaluation metric. Consequently, machine learning is complicated and should not rely on one single model since the correct diagnosis can be controversial in a particular circumstance. Hence, an effective workflow should effortlessly incorporate a diversity of learning models and select the best candidate for a particular input data. In addressing the mentioned problem, the authors present processes that interpret the most appropriate learning models for each of the different clinical datasets as the foundation of developing and recommending diagnostic procedures. The whole process works as (i) automatic hyperparameters tuning for picking the most appropriate learning approach, and (ii) mobile application is developed to support clinical practices. A high F1-measurement has been achieved up to 1.0. Numerous experiments have been investigated on eight real-world datasets, applying several machine learning models, including a non-parameter approach, parameter model, bagging, and boosting techniques.

No abstract

No abstract