Medical Data Classification using a Gravitational Search Algorithm and Artificial Intelligence

Current years have exhibited the prosperity of several types of medical data, for the first time presenting the likelihood to expose the interior mechanism of illness through excavating the huge amount of monitored data for one such individual. Over the past decades, machine learning (ML) approaches were broadly implied for detecting distinct diseases. This enables an initial diagnosis and raises the probability of survival. Many medical data sets are unstable. Because of this, ML classification methods provide biased classification with the majority class. This manuscript derives a Gravitational Search Algorithm with Artificial Intelligence Driven Medical Data Classification (GSAAI-MDC) model. The presented technique majorly intends to accomplish reliable and accurate data classification processes in the medical sector. In order to attain this, the GSAAI-MDC technique applied Deep Support Vector Machine (DSVM) method for the classification of medical data. In addition, the GSA is utilized to enhance the performance of the DSVM method. The simulation analysis of the GSAAI-MDC technique is carried out using benchmark dataset and the results highlighted the superior outcomes of the GSAAI-MDC model over recent approaches.