Categorize data in if else statement

Hi, i am new to matlab and i have a question. Sorry if the questions is simple.

I have created a data (sample size=50)with height and weight to calculate BMI.

% Formula to calculate BMI
BMI=Weight./(Height).^2; 

I have loop for my BMI based on the marks (eg: if BMI<18.5, disp('underweight')...) to categorize them into 'underweight','normal','overweight',etc.

% to loop my BMI and categorize it into 'underweight','normal'...
for i=1:50
if BMI(i)<18.5
    disp('Underweight')
elseif BMI(i)>=18.5&&BMI(i)<24.9
    disp('Normal weight')
elseif BMI(i)>=24.9&&BMI(i)<29.9
    disp('Pre-obesity')
elseif BMI(i)>=29.9&&BMI(i)<34.9
    disp('Obesity class I')
elseif BMI(i)>=34.9&&BMI(i)<39.9
    disp('Obesity class II')
else
    disp('Obesity class III')
end
end

After loop, it will display all the data in

underweight

obese

normal

underweight

.

.

.

for 50 sample size. Is there anyway to show only the total number of underweight , total number of normal , etc

Can I display the data in 'sum of underweight','sum of normal'... instead of displaying all the 50 datasets?

Any advice would be most appreciate.

Answer 1

[Duncan Po]

You can discretize the BMI values into categories and then count them, like this:

>> c = discretize(BMI, [0 18.5 24.9 29.9 34.9 39.9 50], "categorical", ["Under Weight", "Normal weight", "pre-obesity", "obesity I", "obesity II", "obesity III"]);

>> summary(c)

Under Weight 14

Normal weight 7

pre-obesity 1

obesity I 6

obesity II 8

obesity III 14

[poor kid]

Hi! This helped. But i have changed the ['underweight','normal'...] into {'underweight','normal'...}.

c = discretize(BMI, [0 18.5 24.9 29.9 34.9 39.9 50], "categorical", {"Under Weight", "Normal weight", "pre-obesity", "obesity I", "obesity II", "obesity III"});

I can discretize my BMI according BMI_status(underweight,normal,etc) by using only the way you teach me (discretize). But I have to use for loop in this work, may i know is there anyway to loop without displaying the output of the loop?

Once again, Thank You !

[Duncan Po]

You can use a for loop instead of discretize to populate a categorical array:

c = categorical(nan(50,1),1:6,["Underweight", "Normal Weight", "Pre-obesity", "Obesity Class I", "Obesity Class II", "Obesity Class III"]); % preallocate with <undefined> elements

for i=1:50

if BMI(i)<18.5

c(i) = "Underweight";

elseif BMI(i)>=18.5&&BMI(i)<24.9

c(i) = "Normal weight";

elseif BMI(i)>=24.9&&BMI(i)<29.9

c(i) = "Pre-obesity";

elseif ...

end

[poor kid]

Thank you !

Answer 2

[Manas Minnoor]

Hello,

You may create an array of length 6, and increment a particular index of this array each time a particular weight category is accessed (you may put it after the display command). This way you are maintaining a counter for each weight category.

For a more elegant/complicated solution, you may have a look at Maps:

https://in.mathworks.com/help/matlab/map-containers.html

Hope this helps.

[poor kid]

Thanks !

Answer 3

[Girijashankar Sahoo]

Weight=randi([1 100],1,50);
Height=randi([1 3],1,50);
BMI=Weight./(Height).^2; 
% to loop my BMI and categorize it into 'underweight','normal'...
for i=1:50
if BMI(i)<18.5
    disp('Underweight')
    str(i)=["Underweight"];
elseif BMI(i)>=18.5&&BMI(i)<24.9
    disp('Normal weight')
    str(i)=["Normal weight"];
elseif BMI(i)>=24.9&&BMI(i)<29.9
    disp('Pre-obesity')
    str(i)=["Pre-obesity"];
elseif BMI(i)>=29.9&&BMI(i)<34.9
    disp('Obesity class I')
    str(i)=["Obesity class I"];
elseif BMI(i)>=34.9&&BMI(i)<39.9
    disp('Obesity class II')
    str(i)=["Obesity class II"];
else
    disp('Obesity class III')
    str(i)=["Obesity class III"];
end
end
Underweight=sum(count(str,"Underweight"))
Normalweight=sum(count(str,"Normal weight"))
Pre_obesity=sum(count(str,"Pre-obesityt"))
Obesity_class_I=sum(count(str,"Obesity class I"))
Obesity_class_II=sum(count(str,"Obesity class II"))
Obesity_class_III=sum(count(str,"Obesity class III"))

[poor kid]

Hi, thank you for the answer. But it still showing the 50 variables for the loop part...

Results:

Underweight =

0

Normalweight =

0

Pre_obesity =

0

Obesity_class_I =

0

Obesity_class_II =

0

Obesity_class_III =

0

Normal weight

Underweight =

0

.

.

.

Btw, thank again !