Comparative analysis of heart failure prediction using machine learning models

Abstract

Heart  failure  is  a  critical  health  problem  worldwide,  and  its  prediction  is  a major  challenge  in  medical  science.  Machine  learning  has  shown  great potential  in  predicting  heart  failure  by  analyzing  large  amounts  of  medical data. Heart failure prediction with the help of machine learning classification algorithms  involves  the  use  of  models  such  as  decision  trees,  logistic regression,  and  support  vector  machines  to  identify  and  analyze  potential risk factors for heart failure. By analyzing large datasets containing medical and  lifestyle-related  variables,  these  models  can  accurately  predict  the likelihood  of  heart  failure  occurrence  in  individuals.  In  our  research,  the heart  failure  prediction  and  comparison aredone  using logistic  regression,K-nearest  neighbor(KNN),support  vector  machines(SVM),  decision tree  and  random  forest  The  accurate  identification  of  high-risk  individuals enables  early  intervention  and better  management of heart  failure,  reducing the  risk  of  mortality  and  morbidity  associated  with  this  condition.  Overall, machine learning algorithms play a major role in improving the accuracy of heart  failure  risk  assessment,  allowing  for  more  personalized  and  effective prevention and treatment strategies.