The prevalence of cardiovascular diseases (CVD) makes it one of the leading reasons of death worldwide. Reduced mortality rates may result from early detection of CVDs and their potential prevention or amelioration. Machine learning models are a promising method for identifying risk variables. In order to make accurate predictions about cardiovascular illness, we would like to develop a model that makes use of transfer learning. Our proposed model relies on accurate training data, which was generated by careful Data Collecting, Data Pre-processing, and Data Transformation procedures.

Wireless Sensor Network (WSN) includes large quantities of sensing node used in various situations to collect data. WSN considers applications to collect data from remote locations, for example in environment surveillance, military, transport protection, etc. An energy efficient routing protocol is essential to handle the discharged electricity of the system and to minimize traffic and upstairs during data broadcast phases to fulfill the sensing, communication and processing tasks of the WSN. The biggest problem in the WSN is that energy resources are small. In this study, an energy-efficient routing protocol improves energy efficiency with the life span of sensor nodes by using optimization algorithm Pigeon Inspired Optimization (PIO). Clustering technique i.e. Total Generalized Variation Fuzzy c-means clustering (TGVFCMS) is used to select the Cluster Head (CH) and CH folds and bandage the information and direct it to the board node. By its fitness feature, PIO helps to initialize the centroid. After MATLAB simulation, the energy consumption rate was reduced to 25.03% while using PIO. Finally, comparisons are made between the work proposed and the existing current work to assess the effectiveness of the work proposed.