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Problem statement For this program, you are to implement a simple machine-learning algorithm that uses a rule-based classifier to predict whether or not a particular patient has diabetes. In order to do so, you will need to first train your program, using a provided data set, to recognize a disease. Once a program is capable of doing it, you will run it on new data sets and predict the existence or absence of a disease. While solving this problem, you are to use a procedural approach in Java. Decompose your program into methods and use small lists where appropriate, i.e. you are only to use data types covered in chapters 1-6 and chapter 10. Training In order to train your program to recognize diabetes, you need to use a data set that contains both data and diagnoses (this is the learning phase). Your program is to ask the user to enter the name of the training file. You may assume that the file will be a txt file. Each line on that file constitutes one patient record. Each patients record consists of 6 attributes and a numerical diagnosis. From our perspective, it does not matter much what these attributes signify. The only important observation here is that the attributes are floating point numbers delimited by tabs. If an attribute is missing, a question mark replaces a number. In your program, treat question marks as zeros. As far as diagnosis is concerned (the 7th value listed on a line), any value0.65 indicates a presence of a disease, while a value0.65 indicates its absence. You may assume that all data in the file will be valid, i.e there is no need to handle user error or file errors. Sample file train.txt is provided Here is a sample line from the input file 25 175.6 101.14 159.4 Since the last element is a zero, this patient has no diabetes. In case you are wondering, the first 6 attributes are: age, sex, random blood sugar level fasting blood sugar level, oral glucose tolerance level, hemoglobin A1c. Based on the input data that follows this format, the program is to create a classifier that will be used later on to process new patients and make diagnostic predictions for these new patients. The classifier is created in the following fashion. For all patients with no diabetes, for each attribute, we calculate the average value of that attribute. For all patients with diabetes, for each attribute, we calculate the average value of that attribute. After processing all the patients, we end up with two sets of averages-one set for healthy patients (use a list) and one set for ill patients (use a list). In short, as you process a line, you need to first determine whether you are dealing with a healthy or ill patient and update your averages accordingly The picture below illustrates the idea (one row in either table is one line in a file). Health ents Ill patients 13 51 10 4 2 16 61 13 54 11 52 14 | S| 3 4 300 115 0 3 10 0Here is a sample line from the input file 175.6 25 101.14 159.4 Since the last element is a zero, this patient has no diabetes. In case you are wondering, the first 6 attributes are: age, sex, random blood sugar level, fasting blood sugar level, oral glucose tolerance level, hemoglobin A1. Based on the input data that follows this format, the program is to create a classifier that will be used later on to process new patients and make diagnostic predictions for these new patients. The classifier is created in the following fashion. For all patients with no diabetes, for each attribute, we caethe average value of that attribute. For all patients with diabetes, for each attribute, wecalate the average value of that attribute. After processing all the patients, we end up with two sets of averages-one set for healthy patients (use a list) and one set for ill patients (use a list). In short, as you process a line, you need to first determine whether you are dealing with a healthy or ill patient and update your averages accordingly The picture below illustrates the idea (one row in either table is one line in a file). Healt atients Ill patients 13 | 51 10 42 16 6 1 13 54 11 5 2 14 5 3 4 300 1 15 |0 1 0 0 3 10 0 135 Average of healthy patients Average of ill patients Once you have these two averages calculated, you need to find a midpoint (mean) between them-these are called the class separation values. 13 5 Average of healthy patients Average of ill patients 8 7.5 Class separation valuesYour program needs to print the averages of healthy patients, ill patients,ad class separation values to the screen. This is the sample run of the beginning of the program (blue highlighting indicates user input) Enter the name of the training set file: train.txt Healthy patients averages: 37.50,0.00,182.80,107-79,164.47,3.65) Ill patients averages: [56.50,1.00,255.72,134.95,277.06, 8.15] Separator values: [47.00,0.50,219.26, 121.37,220.76,5.90] Run Classifier Once you know class separation values, you can run your program on new sets and make predictions for new patients that havent been diagnosed yet. This is where the second input file comes in. The file will be in a csv format and each line in that file will model one patient: his/her id and 6 attributes described above. The attributes will be in the same exact order as in the training file and will be separated by commas. You may assume that all data in the file will be valid, i.e. there is no need to handle user error or file errors. Sample file set1 . csv is provided For each of these patients, you need to compare his attributes to the separator values. If a patients attribute has a higher value than the corresponding separator value, then the attribute needs to be labeled as at risk. Overall, if a patient has more than half at risk attributes, the patient is classified as ill. In the picture below, we are dealing with an ill patient since this patient is above the norm on more than half of the attributes. 47 0.5 219.26 121.37 220.76 5.9 Class separator values 63 1230.45 127.9 203 4.2 Patient values Once you determine if a patient should be diagnosed as having diabetes, you need to print that patients id and diagnosis to results.csv (sample file provided) use 1, if 4 attributes are above average use 2, if 5 attributes are above average use 3, if all attributes ae above average Finally, print to the screen how many patients you processed, how many received diagnosis codes 1, 2, and 3 This is the sample run of the next phase of the program (blue highlighting indicates user input)Enter the name of the training set file: train.txt Healthy patients averages: 37.50,0.00,182.80,107.79,164.47,3.65 Ill patients averages: [56.50,1.00, 255.72,134.95, 277.06,8.15] Separator values: [47.00,0.50,219.26,121.37,220.76,5.90] Run Classifier Once you know class separation values, you can run your program on new sets and make predictions for new patients that havent been diagnosed yet. This is where the second input file comes in. The file will be in a csv format and each line in that file will model one patient: his/her id and 6 attributes described above. The attributes will be in the same exact order as in the training file and will be separated by commas. You may assume that all data in the file will be valid, i.e. there is no need to handle user error or file errors. Sample file setl.csv is provided For each of these patients, you need to compare his attributes to the separator values. If a patients attribute has a higher value than the corresponding separator value, then the attribute needs to be labeled asat risk. Overall, if a patient has more than half at risk attributes, the patient is classified as ill. In the picture below, we are dealing with an ill patient since this patient is above the norm on more than half of the attributes. 7 0.5 219.26 121.37 220.76 5.9 Class separator values 63 1230.45 127.9 203 4.2 Patient values Once you determine if a patient should be diagnosed as having diabetes, you need to print that patients id and diagnosis to results.csv (sample file provided) . use 1, if 4 attributes are above average . use 2, if 5 attributes are above average . use 3, if all attributes are above average Finally, print to the screen how many patients you processed, how many received diagnosis codes 1, 2, and 3 This is the sample run of the next phase of the program (blue highlighting indicates user input) Enter the name of file containing patient data: set1.csv Number of patients: Diagnosis code 1: Diagnosis code 2: Diagnosis code 3: Check results.csv for specific patients and their codes 303 79

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Answer #1

Answer: fromfuture import with statement #COUNT FOR HEALTHY PATIENTS goodCnt0i att1=0 att2=0 att3=0 att4=0 att5=0 #COUNT FORbadCountt-1 #HEALTHY PATIENTS AVERAGE Hatt1-att1/goodCnt Hatt2-att2/goodCnt Hatt3-att 3/goodCnt Hatt Hatt5-att5/goodCnt Hatt6if float (st [6])-0: ng+=1 ntwnt+i1 else: Accuracy(ng/geodCnt) pant Accuracy : print Accuracy #DIAGNOSING TEST DATA SET lencode:

from __future__ import with_statement
#COUNT FOR HEALTHY PATIENTS
goodCnt=0;
att1=0
att2=0
att3=0
att4=0
att5=0
att6=0
#COUNT FOR ILL PATIENTS
badCount=0;
attA=0
attB=0
attC=0
attD=0
attE=0
attF=0
attG=0
#READING TRAINING DATA SET
filen=raw_input("enter the name of the training set file:")
with open(filen,'rb') as f:
for temp in f:
st=temp.split(",")
#HEALTHY PATIENTS
if float(st[6])==0:
att1+=(float(st[0]))
att2+=(float(st[1]))
att3+=(float(st[2]))
att4+=(float(st[3]))
att5+=(float(st[4]))
att6+=(float(st[5]))   
goodCnt+=1
#ILL PATIENTS
else:
attA+=(float(st[0]))
attB+=(float(st[1]))
attC+=(float(st[2]))
attD+=(float(st[3]))
attE+=(float(st[4]))
attF+=(float(st[5]))   
badCount+=1
#HEALTHY PATIENTS AVERAGE
Hatt1=att1/goodCnt
Hatt2=att2/goodCnt
Hatt3=att3/goodCnt
Hatt4=att4/goodCnt
Hatt5=att5/goodCnt
Hatt6=att6/goodCnt
#ILL PATIENTS AVERAGE
HattA=attA/badCount
HattB=attB/badCount
HattC=attC/badCount
HattD=attD/badCount
HattE=attE/badCount
HattF=attF/badCount

#PRINT HEALTHY AND ILL PATIENTS AVERAGE
print "healthy patients' averages:"
print Hatt1,Hatt2,Hatt3,Hatt4,Hatt5,Hatt6

print "Ill patients' averages:"
print HattA,HattB,HattC,HattD,HattE,HattF

#FIND SEPARATOR VALUES
satt1=(Hatt1+HattA)/2
satt2=(Hatt2+HattB)/2
satt3=(Hatt3+HattC)/2
satt4=(Hatt4+HattD)/2
satt5=(Hatt5+HattE)/2
satt6=(Hatt6+HattF)/2

#DISPLAY SEPARATOR VALUES
print "Separator values:"
print satt1,satt2,satt3,satt4,satt5,satt6

#FINDING ACCURACY
ng=0;
nt=0
with open(filen,'rb') as f:
for temp in f:
st=temp.split(",")
if float(st[6])==0:
ng+=1
nt=nt+1
else:
nt+=1
Accuracy=(ng/goodCnt)

print "Accuracy:"
print Accuracy

#DIAGNOSING TEST DATA SET   
infilename=raw_input("enter the name of the test set file:")
outfile=raw_input("Enter the name of the output file:")
ot=open(outfile,'wb')
with open(infilename,'rb') as f:
for temp in f:
st=temp.split(",")
att1=(int(st[0]))
att2=(float(st[1]))
att3=(float(st[2]))
att4=(float(st[3]))
att5=(float(st[4]))
att6=(float(st[5]))
att7=(float(st[6]))
if((att2>satt1) or (att3>satt2) or (att4>satt3) or (att5>satt4) or (att6>satt5) or (att7>satt6)):
st=str(att1)+",Ill"
else:
st=str(att1)+",Healthy"
ot.write(st) #WRITING TO FILE
ot.write(" ")
ot.close()#CLOSE OUTPUT FILE
print "Done"

Note: Due to time constraint i am not able to prepare the flowchart

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