Human health condition identification method based on BP neural network

A technology of BP neural network and recognition method, which is applied in the field of human health status recognition, and can solve problems such as not comprehensively considering psychological scales

Inactive Publication Date: 2016-12-07
夏茂
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the current medical institutions and related health institutions or academic institutions do not comprehensively con

Method used

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  • Human health condition identification method based on BP neural network
  • Human health condition identification method based on BP neural network
  • Human health condition identification method based on BP neural network

Examples

Experimental program
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Effect test

Embodiment 1

[0244] A man, 59 years old, vital capacity, grip strength, red blood cell count, reaction time, step test, body mass index, fasting blood glucose, heart rate, blood lipids, white blood cell count, bone density, waist-to-hip ratio, 2hPPG, diastolic blood pressure, systolic blood pressure, platelet count, Serum cholesterol, HAMD, HAMA, Yale-Brown obsessive-compulsive scale, Scl90 symptom self-rating scale, PSQI, MAST, FTND and the actual measured values ​​of 25 indicators of average daily working hours are 3769.9mL, 30.9kg, 5.8×10, respectively 12 / L, 0.45, 43.11, 19.9kg / m 2 , 4.5mmol / L, 87 beats / min, 180.95mg / dL, 8.14×10 9 / L, -0.59,0.77,7.1, 71mmHg, 100mmHg, 2.2×10 11 / L, 5.4mmol / L, 3,3,4,34,4,3,1,11. His test result is (0.5799,0.2747,0.1455), which is (1,0,0), which belongs to the healthy category.

Embodiment 2

[0246] A male, 51 years old, vital capacity, grip strength, red blood cell count, reaction time, step test, body mass index, fasting blood glucose, heart rate, blood lipids, white blood cell count, bone density, waist-to-hip ratio, 2hPPG, diastolic blood pressure, systolic blood pressure, platelet count, Serum cholesterol, HAMD, HAMA, Yale-Brown obsessive-compulsive scale, Scl90 symptom self-rating scale, PSQI, MAST, FTND and the actual measured values ​​of 25 indicators of average daily working hours are 3936.4mL, 24.1kg, 4.64×10 12 / L, 0.58, 61.36, 17.4kg / m 2 , 3.4mmol / L, 53 beats / min, 147.99mg / dL, 5.46×10 9 / L, -0.34,0.63,10.3,76mmHg,113mmHg,2.3×10 11 / L, 3.7mmol / L, 1, 4, 5, 22, 2, 0, 1, 10. His test result is (0.3721,0.5073,0.1205), which is (0,1,0), which belongs to the sub-health category.

Embodiment 3

[0248] A man, 44 years old, vital capacity, grip strength, red blood cell count, reaction time, step test, body mass index, fasting blood glucose, heart rate, blood lipids, white blood cell count, bone density, waist-to-hip ratio, 2hPPG, diastolic blood pressure, systolic blood pressure, platelet count, Serum cholesterol, HAMD, HAMA, Yale-Brown obsessive-compulsive scale, Scl90 symptom self-rating scale, PSQI, MAST, FTND and the actual measured values ​​of 25 indicators of average daily working hours are 4016.8mL, 26.6kg, 4.38×10 12 / L, 0.45, 35.85, 22.6kg / m 2 , 8.5mmol / L, 98 times / min, 127mg / dL, 6.90×10 9 / L, -0.09,0.71,5.5,139mmHg, 178mmHg, 1.65×10 11 / L,3.8mmol / L,6,5,3,32,2,4,2,8. His test result is (-0.1246, 0.1652, 0.9595), which is (0, 0, 1), which belongs to the category of chronic diseases.

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Abstract

The present invention relates to a method for identifying human health status based on BP neural network, comprising the following steps: S1, processing the actual measured values ​​of physiological, psychological and behavioral indicators of people who do not suffer from major diseases to obtain the normalization of each indicator value; S2, BP neural network structure initialization; S3, BP neural network recognition training; S4, measure and test the human body to be recognized, and obtain the actual measured value of the selected human body index; S5, use the trained BP neural network to carry out Health status identification. The present invention effectively fuses multiple physiological, psychological, and behavioral index values ​​that reflect the health status of the human body, and provides the health status identification results through the BP neural network model, which not only makes the identification results of the health status of the human body simpler and more intuitive, but also The evaluation results can enable non-professionals to clearly and clearly understand their own health status, and have important guiding significance for the adjustment of human-related life activities and even the prevention and treatment of diseases.

Description

Technical field [0001] The invention belongs to the technical field of human health status recognition, and specifically relates to a human health status recognition method based on a BP neural network. Background technique [0002] With the improvement of people's health care awareness, people pay more and more attention to their health. A joint study by the University of Washington, the University of Southern California and the University of Colorado shows that with the development of aging in the next few decades, the assessment of the human health status becomes more and more important. At the same time, with the continuous advancement of medicine, more and more people gradually shift their focus from diagnosis and treatment in the later stage to prevention in the previous stage. [0003] Based on the current research status in the field of biomedicine, it is known that the body's body mass index (BMI), body temperature, blood pressure, blood lipids, blood sugar, red blood cel...

Claims

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Application Information

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IPC IPC(8): A61B5/0205A61B5/16A61B5/00
CPCA61B5/02055A61B5/165A61B5/7242
Inventor 夏茂魏亚龙
Owner 夏茂
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