A predictive model for prediction of heart surgery procedure

Abstract

Coronary heart disease (CHD) is a disease in which plague in the form of waxy substance builds up inside the coronary arteries. Coronary artery bypass grafting (CABG) is used as treatment on CHD patients but the role of CABG has been challenged by percutaneous coronary intervention (PCI) when it was introduced in 1977. Drug eluting stents (DES) was introduced with the development of PCI. The purpose of this study was to find the potential risk factors that associated with the procedures (CABG and DES) and to model procedure (CABG vs DES) on coronary heart disease male patients aged 45 years old and below. The study sample was among male patients aged 45 years old and below who has undergone CABG or DES procedure at either IJN or HUKM from January 2007 until December 2010. Logistic regression was used to model treatment selection on coronary heart disease with 87.3% of the classification rate. Patient who i) smoke, ii) obese, or ii) had dyslipidemia was significantly associated with DES, and the other factors were prone to have CABG as their treatment.

A hybrid approach to medical decision-making: diagnosis of heart disease with machine-learning model

“…Another comparative study [7] analyzed the performance of random forest and neural network for heart disease prediction. The comparative result shows that neural network performs better than random forest for heart disease prediction.…”

“…The optimal feature subset selected after removing less informative features using iterative feature elimination consists of feature index (0, 2,4,6,7,9,11,12). The proposed model performed with 98.3% accuracy on heart disease prediction using the optimal feature.…”

Heart disease prediction model with k-nearest neighbor algorithm

“…Heart disease is a condition in which a waxy substance is formed in the coronary arteries. This accumulation of plague waxy substance in the arteries makes the blood pumping process to slow down and eventually causes death if not [1]. Heart disease is one of the causes of disease and mortality among the population of the world.…”

“…These errors occur due to lack of experienced specialists in the medical field to accurately and precisely identify the heart disease. Literature survey [1]- [25], shows that the heart disease is still a serious issue which needs further research works in order to address the mortality rate caused by the disease. In this research, we proposed heart disease prediction model by employing k-nearest neighbor (KNN) algorithm to and this research is aimed to answer the following questions: i) What is the right distance measure that produces the optimal accuracy for the KNN on heart disease prediction?…”