Gastric Disorder Analysis Using Hybrid Optimization with Machine Learning

Abstract

The stomach and all of its appendages, which include the oesophagus, duodenum, small intestine, and large intestine, amongst others, all play a crucial function within this system. Stomach dysrhythmias, which are linked to problems with the movement of gastrointestinal contents, affect a significant number of individuals all over the globe. These problems include inappropriate digestion (dyspepsia), nausea (vomiting sensation) for no apparent reason, vomiting, abdominal pain, stomach ulcers, gastroesophageal reflux disease, and other disorders. During the process of finding the anomalies, it is possible that a number of techniques, including as imaging, endoscopy, electrogastrogram, and clinical analysis, will be used. Electrogastrography signals, also known as electrogastrograms (EGG), were captured using surface Ag/AgCl electrodes that were put over the stomach in 20 healthy persons before the data was gathered and pre-processed. The datasets were produced from these signals (8 Females and 12 Males). In addition to this, the datasets were obtained from 10 individuals who were suffering from various stomach illnesses (3 Females and 8 Males). In the stage known as “pre-processing,” which needs the obtained dataset to be treated in advance, any noise that was present in the signal is removed. In order to rid the data of any noise and increase the overall quality of the input data, a technique that is known as the Wiener filter is used. A technique known as Hybrid Grey Wolf Optimization with Particle Swarm Optimization is utilized in the process of feature selection. This algorithm is responsible for removing any extraneous data from the features that have been collected from the signal. The procedure is sped up as a result of this. The classifiers get the qualities that have been chosen as their input in order to carry out an analysis of the many stomach disorders, such as primary gastric lymphoma, gastrointestinal stromal tumour (GIST), and neuroendocrine tumor. This enables the classifiers to do the analysis (carcinoid). The Multi-class Feed Forward Neural Network Classifier (MCFFN) is used to carry out the classification process. This classifier provides the stages along with the classes. The accuracy, sensitivity, and specificity of the classification process are taken into account in the calculation of performance measures.