> For the complete documentation index, see [llms.txt](https://docs.bitdoctor.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.bitdoctor.ai/clinical-measurement-reports/hypercholesterolemia.md). # Hypercholesterolemia ## Description Hypercholesterolemia risk is the likelihood that the user has abnormally high cholesterol levels (defined as a total cholesterol (TC)-to-high density lipoprotein (HDL) cholesterol (“good cholesterol”) ratio of 4.1 or higher) and corresponds to the percentage of people with the user’s risk profile that have an abnormally high TC/HDL ratio. BitDoctor.ai determines the user’s risk of hypercholesterolemia based on blood flow patterns and demographic information. ## Participants Adults (18+ years of age) recruited from several hospital health clinics. ## Additional Data Collection Subjects also received a blood test at the same clinic visit where their cholesterol levels were measured. ## Modeling Approach Blood flow signal was extracted and processed from facial video, and then blood flow features were extracted from blood flow signal. Feature selection was carried out on blood flow and demographic features to identify features predictive of hypercholesterolemia. A machine-learning based classifier was then created to predict whether an individual has hypercholesterolemia based on these features. The model was created/trained using 80% of the subjects. The distribution of sex and hypercholesterolemia status in this group is summarized in Table 17. | Subjects in videos | Composition | | :-------------------------------: | :-----------------------: | | Sex distribution | 46.5 % Male; 53.5% Female | | Hypercholesterolemia distribution | 30% | *Table 17: Hypercholesterolemia distribution of training set* ## Model Performance The Hypercholesterolemia Risk model was then validated for accuracy on an independent portion of the dataset (n=700) that was not used in training - validation set. Then the final accuracy was obtained on an independent portion of the dataset (n=700) that was not used in training - test set. The distribution of sex and hypercholesterolemia status in these groups is summarized in Table 18. | Subjects in videos | Composition in Validation | Composition in Test | | --------------------------------- | ------------------------- | ------------------------ | | Sex distribution | 47.8% Male; 52.2% Female | 46.6% Male; 53.4% Female | | Hypercholesterolemia distribution | 30% | 30% | *Table 18: Hypercholesterolemia distribution of validation and test sets* The accuracy on the test set calculated as area under the curve was 80.3% as shown in Figure 22.
*Figure 22: AUC of Hypercholesterolemia risk prediction* Predictions are displayed to the user as a percentage likelihood of having hypercholesterolemia. A percentage of 45% or more suggests a risk of diabetes, while 55% or more suggests high risk of diabetes.