Toward a Cloud based Disease Diagnosis System Using Sequential Quadratic Programming Approach

The Internet of Things (IoT) relates to the process of utilizing computer networks to plan and model Internet-connected things. The Internet of Things (IoT)-based m-healthcare technologies have provided multi-dimensional functionality and real-time resources over the last few years. These apps provide millions of individuals with a forum to get wellness alerts for a healthy lifestyle constantly. Several aspects of these systems have been revitalized with the introduction of IoT devices in the healthcare sector. This work proposed a data-driven disease signal analytics by inventing a novel combination learning approach. The proposed Combination learning integrates different machine learning models to price disease signal for different options by leveraging the availability of a large amount of data through solving a sequential quadratic programming problem. The proposed approach demonstrates its superiority in prediction accuracy and strong model independence by overcoming traditional model-driven approaches' generalization issue. The findings illustrate the efficacy of the task for an effective disease signal diagnosis. It could be a modern and useful health approach to adopt the proposed procedure with potential changes and incorporate it into a low-cost unit.