Emerging growth of smart devices and application of smart screens in every consumer product enable the peoples to get compulsive interaction with the touch screen. Smart devices with flexible interactive option and evaluation of data analytics and machine learning keeps the humans get addicted with the devices day and night. Even though it offers us numerous advantages over time, Studies revealed that physiological changes are more prospected due to excessive interaction with the smart devices. The proposed system considers ECG data from MIT-BIH dataset followed by keyboard key press and release records as the primary data. The stress of the patients is detected through key press duration as well as ECG report as the secondary data. The proposed system is analyzed using Data aggregation enabled Deep levenberg Marquand model (DA-DLM). The system performance is evaluated using accuracy. The proposed system achieved the average accuracy of 90%. © 2024 Elsevier B.V., All rights reserved.