Body mass index (BMI) is a medical screening tool that measures the ratio of your height to your weight to estimate the amount of body fat you have. Healthcare providers calculate BMI by using weight in kilograms (kg) divided by the square of height in meters (m2).
Create a Python program to determine the body-mass index of a collection of six individuals. Your program should prompt for a list of six names first. Then, using a for loop, it should successively prompt the user for the height in inches and weight in pounds of each individual. Each prompt should display the name of the individual whose height and weight is to be input. Validate that input for height and weight are positive (using a loop). It should call a function that accepts the height and weight as parameters and returns the body mass index for that individual using the formula: BMindex = weight 703 / height2. (eg. 200lb, 6ft(72in) would be: BMindex = (200*703)/(72*72) = 27.1219 ). That body mass index should then be appended to a 2nd "parallel" array. Using another loop it should traverse the array of body mass indices and call another function that accepts the body mass index as a parameter and returns whether the individual is underweight, normal weight or overweight. The number of individuals in each category should be counted and displayed. The Main program should only have function calls. All the code is actually in the functions with arguments and returns. No globals. Design you functions carefully. Note: at least three loops and at least three (maybe as many as 6 or 7) functions.
Body Mass Index Program Python
You likely already know that Python starts indexing at zero. So why did we use sys.argv[1] rather than sys.argv[0] to access the first command line argument value? That is because the first value, sys.argv[0], is the program name. So in our case, sys.argv[0] would be equal to 'bmi_calc.py'.
Tanita's wide variety of professional analyzers provide a detailed full body and segmental body composition analysis - weight, impedance, body fat percentage, body fat mass, body mass index (BMI), fat free mass, estimated muscle mass, total body water, and basal metabolic rate (BMR) for the entire body by using Bioelectrical Impedance Analysis (BIA) or Direct-Segmental Bioelectrical Impedance Analysis (DSM-BIA) technology (two of the most thorough and reliable ways to measure body composition). All scales are built with Tanita's high standards, and many products feature a convenient carrying case, height rod, USB (Type B) & RS232 outputs, software, and a remote display, guaranteeing #long-lasting performance, even in the most demanding healthcare environments.
As the world-leader in providing scales for healthcare professionals, Tanita scales are more affordable and convenient than DEXA (dual energy X-ray absorptiometry) or hydrostatic weighting, yet offer accuracy used in measuring multiple frequency BIA. Our scales provide insight into such indicators as weight loss, caloric intake, diet and #exercise, adipose tissue, and total body water (intracellular and extracellular water) in both pounds and kilograms - making them useful for fitness gyms and clubs as well. #Some models feature stadiometers (height rods), are EMR compatible, are Welch Allen compatible, and wireless output, but all are ISO 9001 certified. TANITA is the only scale trusted by the NCAA Wrestling Championships, USA Wrestling, the Midlands Wrestling Championships, the Reno Tournament of Champions, the Dan #Gable International Wrestling Institute #and Museum as well as countless other tournaments, programs and organizations worldwide
Abstract:One of the major challenges in the post-genomic era is elucidating the genetic basis of human diseases. In recent years, studies have shown that polygenic risk scores (PRS), based on aggregated information from millions of variants across the human genome, can estimate individual risk for common diseases. In practice, the current medical practice still predominantly relies on physiological and clinical indicators to assess personal disease risk. For example, caregivers mark individuals with high body mass index (BMI) as having an increased risk to develop type 2 diabetes (T2D). An important question is whether combining PRS with clinical metrics can increase the power of disease prediction in particular from early life. In this work we examined this question, focusing on T2D. We present here a sex-specific integrated approach that combines PRS with additional measurements and age to define a new risk score. We show that such approach combining adult BMI and PRS achieves considerably better prediction than each of the measures on unrelated Caucasians in the UK Biobank (UKB, n = 290,584). Likewise, integrating PRS with self-reports on birth weight (n = 172,239) and comparative body size at age ten (n = 287,203) also substantially enhance prediction as compared to each of its components. While the integration of PRS with BMI achieved better results as compared to the other measurements, the latter are early-life measurements that can be integrated already at childhood, to allow preemptive intervention for those at high risk to develop T2D. Our integrated approach can be easily generalized to other diseases, with the relevant early-life measurements.Keywords: body weight; genetic variations; GWAS; metabolic disease; obesity; sex difference; UK-Biobank
In this article, we are going to learn how to calculate the Body Mass Index (BMI) using Python. Body Mass Index is also known as the Quetelet Index. It is a value calculated using the weight(in Kg) and height(in meter) of a particular person whether male or female. BMI is defined as the body mass upon the square of the body height. The unit of BMI is Kg/m2. 2ff7e9595c
Comments