The Application of PROMETHEE and K-means Clustering Techniques for Enhancing Robotic Assistance in Bariatric Surgery (Global Journal of Sciences, 2025)
Robotic bariatric surgery (RBS) uses robotics in weight loss operations. The robotic system has been tremendous in the field of bariatric surgery (BS) owing to its overwhelming advantages. As a result, surgeons perform RBS by controlling robotic arms equipped with surgical equipment from a console. In this study, our focus is to evaluate the BS dataset and techniques such as Roux-en-Y gastric bypass (RYGB), laparoscopic vertical sleeve gastrectomy (LGSV), adjustable gastric band (AGB), mini gastric bypass (MGB), single anastomosis duodenal bypass (SADI-S), open RYGB, biliopancreatic diversion with duodenal switch (BPD-DS), and mini gastric bypass one anastomosis gastric bypass (MGB - OAGBP). We used the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) and K-mean clustering analysis to look at how well RBS could help with surgery outcomes. Our result showed that the K-mean of 11 neurons input layer has 100 Self-Organizing Maps (SOM). Also, the PROMETHEE result showed that MGB-OAGBP ranked highest with 0.0056 as the net flow value. The integration of PROMETHEE net flow values, positive and negative outranking values, and BS predicted values demonstrated that MGB-OAGDP ranked highest. In conclusion, our study shows that PROMETHEE and K-means clustering can improve the efficiency of RBS, which can lead to better outcomes for patients.
Keywords: weight loss; techniques; surgical; robotic bariatric surgery; outcomes PROMETHEE; clustering