XGBoost Algorithm to Predict a Patient’s Risk of Stroke

The negative impact of stroke on society has led to a concerted effort to improve stroke management and diagnosis. As the synergy between technology and medical diagnostics grows, caregivers are creating opportunities for better patient care by systematically exploring and archiving patient records. The ubiquitous growth of artificial intelligence and its medical applications has improved the efficiency of healthcare systems for patients requiring long-term personal care. Today, chronic diseases such as stroke are the world’s leading cause of death. Stroke can be caused by a number of factors. By measuring recorded values of patient characteristics such as heart rate, cholesterol levels, blood pressure, diabetes etc., this information can help doctors to make decisions about patient care, in order to predict a possible onset of the disease. Because most stroke diagnosis and prediction systems are image analysis tools such as CT or MRI, which are expensive and not available 24/7 in some African hospitals in general and Senegal in particular. We therefore use a dataset to predict stroke and compare its results with those of other models using the same data. We find that Xgboost, depending on the characteristics of the data, is the algorithm that can effectively predict stroke, and the results obtained are superior to those of other models.