Improving cardiovascular disease risk prediction with machine learning using mental health data: a prospective uk biobank study

Background Robust and accurate prediction of cardiovascular disease (CVD) risk facilitates early intervention to benefit patients. It is well-known that mental disorders and CVD are interrelated. Nevertheless, psychological factors are not considered in existing models, which use either a limited number of clinical and lifestyle factors, or have been developed on restricted population subsets. Objectives To assess whether inclusion of psychological data could improve CVD risk prediction in a new machine learning (ML) approach. Methods Using a comprehensive, long-term UK Biobank dataset (n=375,145), we examined the correlation between CVD diagnoses and traditional and psychological risk factors. An ensemble ML model containing five constituent algorithms [decision tree, random forest, XGBoost, support vector machine (SVM), and deep neural network (DNN)] was tested for its ability to predict CVD risk based on two training datasets: one using traditional CVD risk factors alone, or a combination of traditional and psychological risk factors. Results Our ensemble ML model could predict CVD with 71.31% accuracy using traditional CVD risk factors alone. However, by adding psychological factors to the training data, accuracy dramatically increased to 85.13%. The accuracy and robustness of our ensemble ML model outperformed all five constituent learning algorithms. Re-testing the model on a control dataset to predict bone diseases returned random results, confirming specificity of the training data for prediction of CVD. Conclusions Incorporating mental health assessment data within an ensemble ML model results in a significantly improved, highly accurate, state-of-the-art CVD risk prediction. AUTHOR APPROVAL All authors have seen and approved the manuscript. COMPETING INTERESTS The authors declare no competing interests. DATA AVAILABILITY STATEMENT All data needed to evaluate the conclusions in the paper are present in the paper or in the supplementary materials. In addition, we used UK Biobank in this study: [www.ukbiobank.ac.uk][1]. FUNDING No funding. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement No funding. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data needed to evaluate the conclusions in the paper are present in the paper or in the supplementary materials. In addition, we used UK Biobank in this study: [www.ukbiobank.ac.uk][2]. [1]: http://www.ukbiobank.ac.uk [2]: https://www.ukbiobank.ac.uk