01935nas a2200301   4500000000100000008004100001260003700042653002100079653002000100653003000120653003200150653002200182653002000204100001500224700001600239700001600255700001300271700001400284700001200298700001800310700001600328245014500344856007800489300000900567490000700576520103600583022001401619       2025                            d  bPublic Library of Science (PLoS)10aMachine learning10aImbalanced Data10aClassification Algorithms10aParticle Swarm Optimization10aStroke Prediction10aChagas' disease1 aCoimbra AG1 aOliveira CG1 aLibório MP1 aMannan H1 aSantos LI1 aFusco E1 aD’Angelo MF1 aBukhari SNH00aApproaches for handling imbalanced data used in machine learning in the healthcare field: A case study on Chagas disease database prediction  uhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0320966  a1-190 v203 a<jats:p>Machine learning has increasingly gained prominence in the healthcare sector due to its ability to address various challenges. However, a significant issue remains unresolved in this field: the handling of imbalanced data. This process is crucial for ensuring the efficiency of algorithms that utilize classification techniques, which are commonly applied in risk management, monitoring, diagnosis, and prognosis of patient health. This study conducts a comparative analysis of techniques for handling imbalanced data and evaluates their effectiveness in combination with a set of classification algorithms, specifically focusing on stroke prediction. Additionally, a new approach based on Particle Swarm Optimization (PSO) and Naive Bayes was proposed. This approach was applied to the real problem of Chagas disease. The application of these techniques aims to improve the quality of life for individuals, reduce healthcare costs, and allocate available resources more efficiently, making it a preventive action.</jats:p>  a1932-6203