Improving models performance in a data-centric approach applied to the healthcare domain - Valeriano, M. G.; Kiffer, C. R. V.; Lorena, A. C. (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe); 2024: Anais do XII Symposium on Knowledge Discovery, Mining and LearningDO - 10.5753/kdmile.2024.244519, 2024)
Machine learning systems heavily rely on training data, and any biases or limitations in datasets can significantly impair the performance and trustworthiness of these models. This paper proposes an instance hardness data-centric approach to enhance ML systems, leveraging the potential of contrasting the profiles of groups of easy and hard instances on a dataset to design classification problems more effectively. We present a case study with a COVID dataset sourced from a public repository that was utilized to predict aggravated conditions based on parameters collected on the patient’s initial attendance. Our goal was to investigate the impact of different dataset design choices on the performance of the ML models. By adopting the concept of instance hardness, we identified instances that were consistently misclassified or correctly classified, forming distinct groups of hard and easy instances for further investigation. Analyzing the relationship between the original class, instance hardness level, and the information contained in the raw data source, we gained valuable insights into how changes in data assemblage can improve the performance of the ML models. Although the characteristics of the problem condition our analysis, the findings demonstrate the significant potential of a data-centric perspective in enhancing predictive models within the healthcare domain.