Predictive Modelling of Heart Disease and the Role of Comorbidities in Patient Outcome

Heart disease is one of the major worldwide health issues, and machine learning presents exciting opportunities for the medical field for early diagnosis and to improve prediction accuracy. This work aims to create a predictive model for heart disease based on machine learning. To do this, we used data from UCI and PhysioNet. We attempted to learn more about how comorbid medical disorders affect patient readmission and mortality rates by investigating their effects. A maximum accuracy of 88.52% was achieved in our studies utilizing a variety of methodologies, indicating the potential of machine learning in this field. An important finding from our analysis of patient data related to heart failure is that the readmission and mortality rates increase with the number of comorbidities. This highlights the importance of thorough patient monitoring and preventive care practices. These findings show the importance of machine learning in diagnosing high-risk patients and directing clinical judgment, which ultimately leads to better health outcomes and more effective healthcare administration systems around the world.