MACHINE LEARNING APPLIED TO THE EARLY DETECTION OF HEART ATTACKS IN PATIENTS WITH TERMINAL CHRONIC KIDNEY DISEASE

Manzanares B.W.M.; Demanuel D.R.R.; Vera Y.P. · Journal of Theoretical and Applied Information Technology (2025)

Resumen

Currently, worldwide one in ten adults suffers from chronic kidney disease. The presence of renal lesions shows this disease. Causes the death of at least 2.4 million people a year; it is therefore appropriate to study and develop solutions to reduce the possibility of death. This study aims to develop a predictive model to aid in detecting heart attacks in patients suffering from this disease. To achieve this, six algorithms, Random Forest, XgBoost, Adaboost, Decision Tree, Support Vector Machine, and Gradient Boosting, were applied to construct the model. Statistical comparison was then performed using F1-score, Accuracy, Precision, Area Under the Curve, Recall, MCC and Kappa metrics to detect the best model. Adaboost was obtained as the best algorithm for the construction of models of the same nature. As a result, a model was developed to help predict a heart attack in people with chronic renal failure. This model allows classifications or predictions of this forecast to be made with good results and helps to reduce the risk of death in patients due to its high percentage of effectiveness. It could also be a starting point for future models that treat the same disease. © Little Lion Scientific.

Instituciones: Universidad Nacional de San Agustín de Arequipa