Using GBC Algorithm to Optimize Support Vector Machine Parameters for Predicting the Relationship between Cancer and Cardiac Infarction: A Case Study

Awareness of cancer increases the probability of neurotic disorders and stress in the patient. Also, stress increases the risk of myocardial infarction. The present study aimed to determine the probability of a heart attack in cancer patients based on the GBC algorithm. In this study, data were collected from the database of Shahid Sadoughi subspecialty hospital in Yazd. The medical records of 1679 patients with heart attack were studied, of which 81 ones belonged to patients with cancer. In the process of selecting features by the proposed model, if cancer is identified as an effective feature, then the relationship between cancer and cardiac infarction will be meaningful. Using the proposed model, the cancer feature was selected to predict the probability of heart attack, which indicated a significant relationship between these two characteristics in patients who were vulnerable to cardiac disease. The predictive accuracy of the proposed model was 0.91 By choosing the cancer feature, the proposed model compared to other models has the least error rate and the most accuracy in predicting myocardial infarction. Naive bias method has maximum error rate and minimum accuracy. The simulation results indicate that in patients who are vulnerable to cardiac disease, after being diagnosed with cancer during the early months, heart attack is possible.