HSS 1101 Study Guide - Body Fat Percentage, Coronary Artery Disease, Hydrostatic Weighing

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purplegrasshopper444

11 May 2022

School

University of Ottawa

Department

Health Sciences

Course

HSS 1101

Professor

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Document Summary

Overweight and obesity: overweight: weight greater than expected for specific height, obesity: excessive accumulation of body fat. Cancer, type 2 diabetes, gallbladder disease, and osteoarthritis. Table 6. 1 : body fat percentage norms for men and women. Body mass index (bmi): weight to height. >30 = obese (three classes of obesity) Measurement: energy from a food one kilogram of fat = approximately 7000 calories. Basal metabolic rate (bmr); resting metabolic rate (rmr) 60 minutes of moderate intensity activity daily. Cardiorespiratory, strength exercises; use large muscle groups. = more harmful than helpful to health, is rarely successful in long term, adverse health conditions (metabolism, cardiovascular) , contributes to development of eating disorders. Plans with choices (do not sacrifice enjoyment) Ketosis: fat used as main fuel source. Glycemic load guidelines: > choose plants: beans, instead of meats > eat nuts > mix carbs with other foods > choose whole grains > regular physical activity. Children of obese parents: increased risk of obesity.

Related Questions

What are prions?

In cell, there are subcellular organelles. They are not full organism. Prions are just proteins. They don't have any other macromolecules. Prions have capacity to form copies through replication. They replicate through free radical and chain mechanism.

Doctor Stainly pruisne work on prions and show it's relation with disease called mad cow disease. ( This is neurodegenerative disease and responsible for harmful effects on brain).

In Australia sheep was infected with prions, caused scarpie disease in sheep. Organs of sloghed sheep were used in cow feedd. When cow consume feed, prions were transmitted to cow and cow start to show unusual behavior.

Symptoms of prions infection are like that of some disease

Kurulyprosy
Insomnia
Dimensia

Detection of prions: Prions can be detected by Western bloting.

Normal prions:

Normal prions are present in our body. They don't have tendency to form rigid aggregate. They are quite less in number. They have following functions

Cellular differentiation
Store elemental surplus that is used in future
Memory enhancement

Molecular basis of prions:There is some difference in structure of Normal and abnormal prions.

Normal prions have molecular weight 33-35KD while abnormal prions have molecular weight 27-30KD
In normal prions at position 178 amino acid aspartate is present while in abnormal prions at 178 position asparagine is present.

Prions diagnosis and analysis:

Brain biopsy....in past for taking sample brain biopsy was used but now a days tonsils biopsy is used for sampling.

Immuno fluorescence:Biopsy sample is taken on slide and it is treated with antibody ( of abnormal prions) which is against normal prions. Speciality of antibody is that when it bind with abnormal prion it will glow. If we capture fluroscene it's mean abnormal prion is present in sample. As abnormal prion antibody was label with fluroscene probe. So patient sample will show fluroscene while normal sample will not show fluroscene.

Transmission of prions: Normally prions can't Cross specie barrier they transmitted through different ways. Like as formite

If a person is effected with prions and he has wound in head and his comb is used by another person so prions can be transferred to other individual. Comb is Carrier of prion.

Prions are very slow acting immune system can't destroy them.

skyalpaca180

In this assignment, we build a simple ANN model for a dataset and we explore some methods to optimize model parameters.

Dependencies

Python > 2.7
Scikit Learn package
Pandas toolkit (If it is needed) Numpy toolkit (If it is needed)

Dataset

This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.

Note: You do not need to download the data. Two subsets of data for training and test is created and posted for downloading. The original data set also is included for your further testing and experimenting.

The list of the fields in order of columns in the data file is:
PregnanciesNumber of times pregnant
GlucosePlasma glucose concentration 2 hours in an oral glucose tolerance test BloodPressureDiastolic blood pressure (mm Hg)
SkinThicknessTriceps skin fold thickness (mm)
Insulin2-Hour serum insulin (mu U/ml)
BMIBody mass index (weight in kg/(height in m)^2)
DiabetesPedigreeFunctionDiabetes pedigree function
Age (years)
OutcomeClass variable (0 No Diabetes or 1 Diabetes) 268 of 768 are 1, the others are 0.

ANNprojectdescription
– Loading the required libraries and modules
To finish this assignment, you may use any module that you think is necessary. These modules includes:
1- Pandas
2- Numpy
3- Matplotlib
4- Sklearn

– Reading the data and performing basic data checks

1- Use describe() method of the dataframe (e.g df.describe()) to output the data dimensions and fields basic stats.

– Creating the train and test datasets

1- Splityourdataintotrain,testand,validation.
2- Splityourdatabasedontheratiooftrain=0.75,validation=0.15and,test=0.10
3- Printoutthetotalnumberofrowsandthenumberofrowsallocatedforeach

category above.
Note1: There are many ways you can achieve splitting dataset.
You can use any method as long as you have a balanced ratio of classes in each category.
4- Useyourtestdatasetonlyforfinaltesting.Youshouldnotusetestdatasetfor adjusting parameters.

– Building your model

1- Useneural_networkclassifiertobuildyournetwork.
2- Thereareafewparametersneedstobeadjustedinthismodel:

a. Hidden_layer_sizes b. Activation
c. Solver
d. Batch_size

e. Learning_rate
f. Learning_rate_init
3- Mostofparametersabovehavedefaultvalues.Youmayrunyournetworkon default values to get an idea about your network
4- Youmayadjustparametersbasedwhatwetalkedintheclassetc

– Network metric and plots

1- To evaluate your network, calculate confusion matrix. For that you may use confusion_matrix function from sklearn.metrics library.
How do you interpret precision and recall?
2- Plot error plot for each iteration (epoch) for both validate and train.
Based on your parameter setting you may see an overfitting problem. Try to spot it if that’s happened.
3- Once you finish your parameter tunning, run your model on your test data set and create a plot for your test the same as previous step

Whatareyourdeliverables?

a. A Jupyter document containing your model and experiment results.

dha2021

HSS 1101 Study Guide - Body Fat Percentage, Coronary Artery Disease, Hydrostatic Weighing

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