Multi Disease Prediction Model by using Machine Learning and Flask API

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Multi Disease Prediction Model by using Machine
Learning and Flask API
Akkem Yaganteeswarudu
Infoshare Systems, Pyramid Softsol Pvt Limited
Hyderabad, India
yaganteeswaritexpert@gmail.com
Abstract-- Many of the existing machine learning models for
health care analysis are concentrating on one disease per
analysis. Like one analysis if for diabetes analysis, one for
cancer analysis, one for skin diseases like that. There is no
common system where one analysis can perform more than one
disease prediction. In this article proposing a system which
used to predict multiple diseases by using Flask API. In this
article used to analyse Diabetes analysis, Diabetes Retinopathy
analysis, Heart disease and breast cancer analysis. Later other
diseases like skin diseases, fever analysis and many more
diseases can be included. To implement multiple disease
analysis used machine learning algorithms, tensorflow and
Flask API. Python pickling is used to save the model behaviour
and python unpickling is used to load the pickle file whenever
required. The importance of this article analysis in while
analysing the diseases all the parameters which causes the
disease is included so it possible to detect the maximum effects
which the disease will cause. For example for diabetes analysis
in many existing systems considered few parameters like age,
sex, bmi, insulin, glucose, blood pressure, diabetes pedigree
function, pregnancies, considered in addition to age, sex, bmi,
insulin, glucose, blood pressure, diabetes pedigree function,
pregnancies included serum creatinine, potassium,
GlasgowComaScale, heart rate/pulse Rate, respiration rate,
body temperature, low density lipoprotein (LDL), high density
lipoprotein (HDL), TG (Triglycerides).
Final models behaviour will be saved as python pickle file.
Flask API is designed. When user accessing this API, the user
has to send the parameters of the disease along with disease
name. Flask API will invoke the corresponding model and
returns the status of the patient. The importance of this
analysis to analyse the maximum diseases, so that to monitor
the patie nt’s conditi on and warn the patients in advance to
decrease mortality ratio.
Keywords-- Flask API, lipoprotein, GlasgowComaScale,
Diabetes, Triglycerides, Mortality
I. INTRODUCTION
During a lot of analysis over existing systems in health
care analysis considered only one disease at a time. For
example, article [1] is used to analyse diabetes, article [2] is
used to analyse diabetes retinopathy, article [3] is used to
predict heart disease [11]. Maximum articles focus on a
particular disease. When any organization wants to analyse
their patient’s health reports then they have to deploy many
models. The approach in the existing system is useful to
analys e only particular disease. Now a day’s mortality got
increased due to exactly not identifying exact disease. Even
the patient got cured from one disease may be suffering
from another disease. In real life, I faced that situation. My
father got cured from the accident. My father got discharged
from hospital but after a few days he got expired. Internally
suffering from heart issues which is not identified. Like this
many instances observed in many people’s life stories.
Some existing systems used few parameters while
analysing the disease. Due to that may be not possible to
identify the diseases which will be caused due to the effect
of that disease. For example, due to diabetes, there may be
chance of heart disease, neuropath, retinopathy, hearing
loss, and dementia.
In this article considered Diabetes analysis, Diabetes
Retinopathy, heart disease and cancer detection data sets. In
future many other diseases like skin diseases can be
included, fever related diseases and many more. This
analysis is flexible that later included many diseases for
analysis. While adding any new disease analysis to this
existing API, the developer has to add the model file related
to the analysis of the new disease.
When developing new disease the developer have to
prepare python picking to save model behaviour. When
using this Flask API, the developer can load pickled file to
retrieve the model behaviour. When user wants to analyse
the patient’s health condition either then can predict a
particular disease or if the report contains parameters which
are used to predict other diseases then this analysis will
produce maximum identification of relevant diseases.
The aim of this article is used to prevent mortality ratio
increasing day by day by warning the patients in advance
based on their health conditions. Due to many diseases
models and predictions done at one place cost of patient
analysis can be reduced.
Proceedings of the Fifth International Conference on Communication and Electronics Systems (ICCES 2020)
IEEE Conference Record # 48766; IEEE Xplore ISBN: 978-1-7281-5371-1
978-1-7281-5371-1/20/$31.00 ©2020 IEEE 1242
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II. PROPOSED WORK AND PROCUDURE FOR
MODEL DESIGN
A. Existing system
Many of existing analysis involved analysing particular
disease. When a user wants to analyse diabetes needs to use
one analysis and same user wants to analyse heart disease
then user has to use one more model. This is a time taking
process. And also if any user having more than one disease
but in existing system if it is able to predict only one disease
then there is a chance of mortality rate increase due to not
able to predict the other disease in advance.
B. Proposed system
In multi disease model prediction, it is possible to
predict more than one disease at a time. So user no need to
traverse many models to predict the diseases. It will reduce
time and also due to predicting multiple diseases at a time
there is a chance of reducing mortality rate.
C. Dataset preparation
For diabetes analysis initially Pima Indian Diabetes
Dataset, the data set which was acquired from a hospital in
Frankfurt, Germany are used. For diabetic retinopathy over
150 GB image data from the UCI machine learning
repository are used. For heart disease analysis Cleveland,
Hungarian and Switzerland heart disease patient’s data sets
are used. And for cancer disease prediction used Breast
Cancer Wisconsin (Diagnostic) Data Set which is available
in machine learning repository. In the current analysis in
addition to those data sets used other live data sets by
visiting corresponding hospitals. The importance of this
analysis is by consulting the doctors collected the necessary
parameters which will cause the disease and also due to that
disease any other disease likely to occur. After doing this
analysis there is a chance of reducing mortality ratio
because if can able to predict the maximum diseases
chances of occurring so that can warn the patients in
advance for treatment
As per industry standards train set and test are prepared.
By using Scikit learn train_test_split method to split the data
as 70 % for training and 30 % for testing are divided.
Example: diabetes_feature_train, diabetes_feature _test,
diabetes_label_train, diabetes_label _test = train_test_split
(diabetes_features, diabetes_label, test_size=0.3,
random_state=0)
B. Machine learning and Deep learning
Article main focus is to build a multi disease prediction
model so machine learning and deep learning techniques
used are briefly summarized here. Diabetes analysis, Heart
disease prediction and cancer detection are analysed by
different machine learning and deep learning techniques.
Like logistic regression, Naïve Bayes [13] classification
algorithm, SVM, Decision tree algorithm, Random forest
algorithm and many more algorithms are used to find the
status of the patient. For diabetes analysis, logistic
regression results 92% accuracy, for heart disease
classification Randomforest yield 95% accuracy and for
cancer detection SVM yield 96 % accuracy.
Diabetes retinopathy analysis contains retina images. So
used the python tensorflow library to analyse the images .
The tensorflow convolution neural networks is used for
building the model and tested with the test set. The built
model produced 91 % accuracy.
III. MODEL BEHAVIOUR SAVING WITH
PYTHON PICKLING
A. Python pickling for heart disease prediction data set
Once the data set is processed with training set and test
set, best algorithm which producing the highest accuracy is
selected. Model behaviour can be saved by using python
pickling. The python pickle [12] model is used to serialize
or de-serializing the python object structure. Python object
can be pickled and it can be saved on disk. The python
pickle file is a character stream which contains all the
necessary information to reconstruct the object in another
script.
Consider hear.csv is a heart disease csv file. To process
the file and saving it as a pickle file, see the below code
For heart disease model file pickling:
from sklearn.ensemble import RandomForestClassifier
import pickle as p
rf = RandomForestClassifier (random_state=x)
rf.fit (X_train, Y_train)
p.dump (rf, open ('final_heartdiseasepred.pickle', 'wb'))
In the above sample code 'final_heartdiseasepred.pickle’, the
pickle file is created.
B. Python pickling for diabetes prediction data set
For diabetes model file pickling:
df=pd.read_csv ("diabetes.csv")
x = df.iloc [:, 0:8]
y = df.iloc [:, 8]
x_train, x_test, y_train, y_test = train_test_split(x, y,
random_state=0)
sv = svm.SVC (kernel='linear')
sv.fit (x_train, y_train)
p.dump (rf, open ('final_diabetes.pickle', 'wb'))
Proceedings of the Fifth International Conference on Communication and Electronics Systems (ICCES 2020)
IEEE Conference Record # 48766; IEEE Xplore ISBN: 978-1-7281-5371-1
978-1-7281-5371-1/20/$31.00 ©2020 IEEE 1243
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C. Diabetes retinopathy prediction and model saving
by tensorflow
Since diabetes retinopathy analysis contains retina
images. So tensorflow model is used to analyse the model
and model behaviour stored in a file
model.compile (loss='categorical_crossentropy',
optimizer='sgd',
metrics= ['accuracy'])
model.fit (X_train, Y_train, epochs=200, validation_data=
(X_valid, Y_valid), verbose=1)
model.save
('/home/Yaganteeswarudu/diabeticRetino2/my_model.h5')
D. Python pickling for cancer prediction data set
from sklearn.svm import SVC
svc_model= SVC ()
svc_model.fit (x_train, y_train)
p.dump (svc, open ('final_cancer.pickle', 'wb'))
IV. MULTI DISEASE PREDICTION MODEL
A. Loading pickle file to predict the disease
Once the model building is finished and model
behaviour saved as pickle file. In this analysis considered 4
diseases, so 4 pickle files are generated. Before analysing
the disease load the all pickle files in the python script
where the multi diseases analysis takes place
Sample code:
heartmodelfile='final_heartdiseasepred.pickle'
diabetesmodelfile='final_diabetes.pickle'
diabetesretinomodelfile='final_cancer.pickle '
diabetereretino='my_model.h5'
heartmodel=p.load (open (heartmodelfile,'rb'))
diabetesmodel=p.load (open (diabetesmodelfile,'rb'))
cancermodel=p.load (open (cancermodelfile, ‘rb'))
retinomodel = load_model (diabetereretino)
B. Data flow
Figure1 represents data flow in analysis. Analysis starts
by pre-processing the data. Pre-processing is required
because, for example a live human being blood pressure
[11] cannot be zero and so on. Those kind of records to be
pre-processed. After pre-processing data set preparation and
model building for the different diseases [13] with available
data sets. All the model behaviours are saved in pickle files.
Flask API designed
Fig. 1. Data flow
Sample code for Heart Disease prediction Flask API:
@app.route ('/heartdiseasecondition', methods= ['POST'])
def heartdiseasecondition():
url="http://localhost:5000/heartdiseaseprediction"
# Get the required inputs from user filled form
data=[[age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,old
peak,slope,ca,thal]]
j_data=json.dumps(data)
headers={'content-type':'application/json','Accept-
Charset':'UTF-8'}
r=requests.post (url, data=j_data, headers=headers)
Once Flask API is designed load the pickle file and return
the patient status to the user.
V. RESULTS ANALYISIS
Once Flask API is designed. Model can be consumed at
front end. Verified by designing sample website
Figure2 shows heart disease input screen. Once user
clicks on get status of heart disease patient it will return
whether patient have heart disease or not
Proceedings of the Fifth International Conference on Communication and Electronics Systems (ICCES 2020)
IEEE Conference Record # 48766; IEEE Xplore ISBN: 978-1-7281-5371-1
978-1-7281-5371-1/20/$31.00 ©2020 IEEE 1244
Authorized licensed use limited to: UNIVERSITY OF BIRMINGHAM. Downloaded on July 22,2020 at 01:34:12 UTC from IEEE Xplore. Restrictions apply.

Fig. 2. Heat disease input screen
Figure 3 represents output of the heart disease
prediction. Once the user enters all details and click on get
heart disease status. Corresponding model will be loaded
and result of heart disease will be shown to the user.
Fig 3: Output of patient status
CONCLUSION
Multi disease prediction model is used to predict
multiple diseases at a time. Here based on the user input
disease will be predicted. The choice will be given to user.
If the user want to predict particular disease or if the user
don’t enter any disease type then based on user entered
inputs corresponding disease model will be invoked and
predicted. The advantage of multi disease prediction model
in advance can predict the probability of occurrence of
various disease and also can reduce mortality ratio.
ACKNOWLEDGMENT
Thanks to the Infoshare Systems, pyramid softsol pvt ltd
management for giving me the opportunity to do analysis on
health care domain.
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Authors:
Akkem Yaganteeswarudu, Finished post-
graduation (M.Tech) in 2012. Started professional career in 2012.
Proceedings of the Fifth International Conference on Communication and Electronics Systems (ICCES 2020)
IEEE Conference Record # 48766; IEEE Xplore ISBN: 978-1-7281-5371-1
978-1-7281-5371-1/20/$31.00 ©2020 IEEE 1245
Authorized licensed use limited to: UNIVERSITY OF BIRMINGHAM. Downloaded on July 22,2020 at 01:34:12 UTC from IEEE Xplore. Restrictions apply.

Worked at Honeywell, ITC InfoTech, Unisys and currently
working at Pyramid Soft Sol as a Data scientist. Having 8+ years
of experience and in Data science having 4+ years of experience.
Published various IEEE papers like preventing suicides of farmers,
Speaking compiler and many more. Having interest in research in
health care domain. Interested to develop health care solutions by
using machine learning and deep learning techniques.
Proceedings of the Fifth International Conference on Communication and Electronics Systems (ICCES 2020)
IEEE Conference Record # 48766; IEEE Xplore ISBN: 978-1-7281-5371-1
978-1-7281-5371-1/20/$31.00 ©2020 IEEE 1246
Authorized licensed use limited to: UNIVERSITY OF BIRMINGHAM. Downloaded on July 22,2020 at 01:34:12 UTC from IEEE Xplore. Restrictions apply.