Construction and Analysis of a Joint Diagnosis Model of Random Forest and Artificial Neural Network for Obesity

Obesity is a significant global health concern since it is connected to a higher risk of several chronic diseases. As a consequence, obesity may be described as a condition that reduces human life expectancy and significantly impacts life quality. Because traditional obesity diagnosis procedures have several flaws, it is vital to design new diagnostic models to enhance current methods. More obesity-related markers have been discovered in recent years as a result of improvements and enhancements in gene sequencing technology. Using current gene expression profiles from the Gene Expression Omnibus (GEO) collection, we identified differentially expressed genes (DEGs) associated with obesity and found 12 important genes (CRLS1, ANG, ALPK3, ADSSL1, ABCC1, HLF, AZGP1, TSC22D3, F2R, FXN, PEMT, and SPTAN1) using a random forest classifier. ALPK3, HLF, FXN, and SPTAN1 are the only genes that have never been linked to obesity. We also used an artificial neural network to build a novel obesity diagnosis model and tested its diagnostic effectiveness using public datasets.

Automated summary

Obesity is a global health concern that increases the risk of chronic diseases and reduces life expectancy and quality of life. Traditional diagnosis methods for obesity have flaws, necessitating the design of new diagnostic models. Recent advancements in gene sequencing technology have led to the discovery of more obesity-related markers. Using gene expression profiles, 12 important genes associated with obesity were identified. An artificial neural network was also used to develop an effective obesity diagnosis model.