Guess Who's Not Showing Up? A Machine Learning Look at Patient No-Shows

Ever heard of the "no-show" issue in healthcare? It's when patients book appointments but don't turn up. We looked at this over five years with 21, 969 patients. Our study created a special model to solve this problem. This model not only predicts no-shows but also helps manage service capacity and cuts down on wasted resources. It even makes life easier for people working in healthcare by not needing expert help with data.

We found that some factors, like age and appointment type, are common no-show predictors. Our method makes picking these factors simpler, so our model can be used anywhere. This makes healthcare providers use resources better and offer services more smoothly. Future plans include adding weather and travel data to see if these affect no-shows. Our approach is new and could make a big difference in healthcare worldwide.

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