Compared to an established FRS prediction algorithm, we found all the ML models (LR, RF, KNN, ANN) had a better prediction validation than FRS for the presence of CAD, moreover, ANN had a better prediction than FRS for the severity of CAD.
Firstly, if more variable data could be added into the training dataset for the ML models, more accurate and individual prediction could be built for our human beings. In our results, all the 29 attributes could be divided into the categories of basic personal information, blood cells examinations, blood biochemistry tests and medical histories. Besides the variables in our research, other special variables were proved to be predictors for the CAD prediction. For instance, in a large Chinese cohort study(15), the data show the correlation between the ABO blood groups and the severity of CAD; And about the obesity with CAD, the Waist-Hip Ratio is considered to positively related to the presence and severity of CAD(16). HDL sub-fractions(17) and micronucleus frequency and nuclear division index (18)are also proved to be the significant indicators to predict the extent and severity of CAD. In the future further study, to add up these special variables to the dataset was a promising step for the risk prediction.
Secondly, ML methods applied to predict the presence and the severity of CAD could build a more personalized and precise model than the traditional risk systems. Not only our results showed this purpose, in the last 5 years, many other original articles were done to prove this strong statement. In a United Kingdom’s research(19), ML models improved the accuracy of the prediction for the 10-year risk for CVD, and the validation was better than the ACC/AHA equation for the risk prediction. In a Korean Study(20), investigators applied the Deep Belief Neural Networks, one of the ML algorithms, into the prediction of CVD, showed accuracy 83.9% and AUC 0.790. Besides CVD, in another USA’s study about heart failure(21), based on the electronic medical records, the ML model had 11% improvement in AUC than the mainstream Seattle Heart Failure Model. In an Arabian investigation(22), four different ML algorithms were used to predict the length of stay in the hospital. Above all, these steps were exciting and also on the way to the individual medicine.
Thirdly, datasets were the fundamental essential for a better prediction rather than the methods, including ML algorithms. In our results, the ML models for the presence was promising, but the results for the severity didn’t achieve our expectation, the performance of FRS was better than KNN, LR and RF. In almost all study we referred, ML models showed a better performance than the traditional equations(19–22). Back to our study population, Group1 and Group4, the total number for two groups was 756, were the datasets for the building of ML model. Literally, the quantity and quality of the datasets could be the limiting factors for the usefulness of ML models.
Generally, there were several limitations of this current study. Firstly, as mentioned before, the dataset was from one-single health organization instead of several different centers. What’s more, because of our inclusive and exclusive criteria, these patients already had a high suspicion of CVD. Secondly, it was acknowledged that the “black box” nature of ML models could be impossible for the interpretation of ML models. Thirdly, if the data loss of an attribute reached 10%, the attribute was removed from the dataset. This process would cause some biases before we knew the specific variable was important for the prediction or not.