title: Predictive models to assess risk of type 2 diabetes, hypertension and comorbidity: machine-learning algorithms and validation using national health data from Kuwait--a cohort study.
abstract: OBJECTIVE
  We build classification models and risk assessment tools for diabetes, hypertension and comorbidity using machine-learning algorithms on data from Kuwait. We model the increased proneness in diabetic patients to develop hypertension and vice versa. We ascertain the importance of ethnicity (and natives vs expatriate migrants) and of using regional data in risk assessment.
  
  
  METHODS
  Retrospective cohort study. Four machine-learning techniques were used: logistic regression, k-nearest neighbours (k-NN), multifactor dimensionality reduction and support vector machines. The study uses fivefold cross validation to obtain generalisation accuracies and errors.
  
  
  METHODS
  Kuwait Health Network (KHN) that integrates data from primary health centres and hospitals in Kuwait.
  
  
  METHODS
  270 172 hospital visitors (of which, 89 858 are diabetic, 58 745 hypertensive and 30 522 comorbid) comprising Kuwaiti natives, Asian and Arab expatriates.
  
  
  METHODS
  Incident type 2 diabetes, hypertension and comorbidity.
  
  
  RESULTS
  Classification accuracies of >85% (for diabetes) and >90% (for hypertension) are achieved using only simple non-laboratory-based parameters. Risk assessment tools based on k-NN classification models are able to assign 'high' risk to 75% of diabetic patients and to 94% of hypertensive patients. Only 5% of diabetic patients are seen assigned 'low' risk. Asian-specific models and assessments perform even better. Pathological conditions of diabetes in the general population or in hypertensive population and those of hypertension are modelled. Two-stage aggregate classification models and risk assessment tools, built combining both the component models on diabetes (or on hypertension), perform better than individual models.
  
  
  CONCLUSIONS
  Data on diabetes, hypertension and comorbidity from the cosmopolitan State of Kuwait are available for the first time. This enabled us to apply four different case-control models to assess risks. These tools aid in the preliminary non-intrusive assessment of the population. Ethnicity is seen significant to the predictive models. Risk assessments need to be developed using regional data as we demonstrate the applicability of the American Diabetes Association online calculator on data from Kuwait.
authors.affiliation: Integrative Informatics, Dasman Diabetes Institute, Dasman, Kuwait.
authors.forename: Bassam
authors.initials: B
authors.lastname: Farran
authors.forename: Arshad Mohamed
authors.initials: AM
authors.lastname: Channanath
authors.forename: Kazem
authors.initials: K
authors.lastname: Behbehani
authors.forename: Thangavel Alphonse
authors.initials: TA
authors.lastname: Thanaraj
keywords: Kuwait; Machine learning; Risk assessment; predictive models