BitDoctor.ai
  • Abstract
  • Introduction
  • Problem Statement
  • Market Insights
  • Preventive Healthcare
  • Unique Value Proposition
  • BitDoctor Blockchain Technology Stack
  • Our AI Technology
    • Training BitDoctor's AI
    • Hardware And Imaging Requirement
  • Clinical Measurement Reports
    • Heart Rate
    • Breathing Rate
    • Irregular Heartbeat
    • Heart Rate Variability
    • Hypertension Risk
    • Type 2 Diabetes Risk
    • Cardiovascular Diseases Risk (incl. Heart Attack & Stroke Risks)
    • Hypercholesterolemia
    • Hypertriglyceridemia
    • Fatty Liver Disease
    • Morning Fasting Blood Glucose
    • Hemoglobin A1C
    • Image-Based Age
  • DePIN Shared Economy
  • Strategic Opportunities
    • Clinical Trial Agencies
    • Preventive Healthcare Brands
    • Active Wear & Equipment
    • Insurance Company
    • Pharmaceutical Company
    • Supplement Company
    • Crypto Firms
  • Tokenomics
    • Token Utility
  • Community
    • About $LiV Points
  • Roadmap
  • Links
  • Appendix
  • Team Info
    • Advisors
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On this page
  • Description
  • Participants
  • Additional Data Collection
  • Modeling Approach
  • Model Performance
  1. Clinical Measurement Reports

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.

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Last updated 4 months ago

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