PCOS Detect using Machine Learning Algorithms

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 01 | Jan 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1214
Kinjal Raut 1, Chaitrali Katkar2, Prof. Dr. Mrs. Suhasini A. Itkar 3
1Final Year Computer Engineering Student, PES Modern College of Engineering, Pune
2Final Year Computer Engineering Student, PES Modern College of Engineering, Pune
3Professor, Dept. of Computer Engineering, PES Modern College of Engineering, Pune, India
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Abstract - Polycystic ovary syndrome (PCOS), also known as
polycystic ovarian syndrome, is hormonal endocrine disorder
among women of reproductive age. Over five million women
worldwide in their reproductive age are suffering from PCOS.
The most common symptoms of this disorder may include
missed periods, irregular periods, or very light periods, it af-
fects in a way that ovaries become large or may contain many
cysts, it can also cause excess body hair, including the chest,
stomach, and hirsutism, can cause weight gain, especially
around the abdomen, Acne or oily skin. The exact pathophysi-
ology of PCOS is not yet known. This heterogenous disorder is
characterized by the ovaries mainly. PCOS is a multifactorial
and polygenic condition. Machine Learning is capable of
"learning" features from very large amount through clinical
practice to diagnose this disorder. This paper put forwards a
solution to this problem which helps in early detection and
prediction of PCOS treatment fromanoptimalandminimal set
of parameters which have been statistically analyzed. The so-
lution is built using machine learning algorithms such asRan-
dom Forest, Decision Tree, Support Vector Classifier, Logistic
Regression, K-Nearest neighbors, XGBRF, CatBoost Classifier.
Key Words: Machine Learning, Polycystic Ovary Syn-
drome, Random Forest, Decision Tree, Support Vector
Classifier, K-Nearest Neighbours, Logistic Regression, K-
Nearest Neighbours.
1. INTRODUCTION
Technology is changing every outlook of our lives making
remarkable transformations in the healthcare industry,
nowadays technology and humans are working hand in
hand. For example, robots performing surgeries once
seemed a fiction but now they are performing critical and
complex surgeries in hospitals.
Machine learning is a subclass of artificial intelligence, it
helps the system learn, identify patterns of datasets, make
logical decisions and performing digital analysis on digital
information including words, numbers, images and clicks.
Machine Learning applications mainly include image recog-
nition, data prediction, Medical Diagnosis – Health Care and
Clinical Care, etc. In this world of technology many advance-
ments are taking place for detection of PCOS and Machine
Learning algorithms are one of them.
PCOS is one of the most widely common endocrinedisorders
that affects 1 in 10 women of childbearing age. The exact
prevalence of PCOS is not known but variable ranging from
2.2% to 26% globally. It was first detailed in 1935 by Stein
and Leventhal as a syndrome manifested by hirsutism, and
obesity associated with enlarged polycystic ovaries.Woman
in reproductive age 15-40 experience hormonal imbalance,
hence PCOS can happen at any age after puberty.
Hormones needed are progesterone, luteinizing hormone
(LH), estrogen and follicle stimulating hormone (FSH). The
common symptoms of PCOS are irregular menstrual cycle,
too much hair, acne, weight gain, darkeningofskin,skintags.
There is a high risk of first trimester miscarriage, in ovaries
inappropriate growth of follicle can be prevented by detect-
ing PCOS at an early stage. Hence detection of PCOS is im-
portant at primary stage. This paper focusesonpredictionof
PCOS.
The main work includes:
1. Selection of most important attributes using feature se-
lection method from the dataset.
2. Applying/Performing machine learning algorithms on
the selected features.
3. Comparing the performed algorithms in order to check
accuracy.
1.1 Literature Review
Over 10 million young generation has been affected globally
with 1 in every 4 four young women having PCOS. The dis-
ease is more common in urban population than rural be-
cause of the lifestyle. The increase in number of PCOS wom-
en is directly correlated with the sedentary lifestyleandlack
of nutritional food, lack of exercise, weight gain and obesity.
Table -1: Summary of Literature review
AUTHORS OBJECTIVES RESEARCH
DESIGN
RESULTS
Palak et al.
[2012]
A method to
automate
PCOS based
on clinical
and meta-
bolic mark-
ers.
Classification
of features
based on
Bayesian and
Logistic Re-
gression.
Among the
two com-
pared models
the best
model built is
Bayesian
classifier
with accura-
cy 93.93%.
Purnama et Detection of Three classi- On C=40
PCOS Detect using Machine Learning Algorithms

al. [2015] Follicles
based on
USG images
by using the
binary folli-
cle images,
feature ex-
traction and
segmenta-
tion
fication sce-
narios were
designed
Neural Net-
work - LVQ,
KNN - Euclid-
ean distance,
SVM - RBF
kernel.
SVM-RBF
kernel
achieved
82% accura-
cy and on
K=5 KNN
achieved
78% accura-
cy.
Denny et al.
[2019]
To over-
come the
time and
cost in-
volved in
various clin-
ical testsand
ovary scan-
ning.
PCOSfeatures
transformed
with PCA
used machine
learning algo-
rithms like
KNN, SVM,
RF, etc.
The best and
accurate
model for the
PCOS detec-
tion came out
Random For-
est with 0.89
acc
Subrato et
al.
[2020]
Data driven
diagnosis of
PCOS using
dataset on
Kaggle re-
pository.
Classifiers
used are as
follows gra-
dient boost-
ing, random
forest,logistic
regression,
RFLR and
methods ap-
plied are
holdout and
cross valida-
tion.
The best test-
ing accuracy
obtained is of
RFLR
91.01%, re-
call value
90%.
Ning-Ning
Xie et al.
[2020]
To identify
gene bi-
omarkers
and build
diagnostic
model
Computa-
tional method
applied by
combining
two machine
learning algo-
rithms such
as ANN, and
Random For-
est
A novel diag-
nostic model
developed
with accura-
cy of AUC:
0.7273 in
microarray
dataset and
0.6488 in
RNA-seq da-
taset.
Priyanka et
al.
[2020]
Classifica-
tion of PCOS
will use
physical
symptoms
and sono-
grams in
which only
the physical
symptoms
will be pre-
sented.
Used differ-
ent algo-
rithms like K-
star, IB1 in-
stance-based,
locally
weighted
learning, De-
cision Table,
M5 rules, Ze-
ro R, Random
Forest and
Random Tree
to classify
and find best
Among dif-
ferent algo-
rithms per-
formed K-
star outper-
formed.
model.
Namarat
Tanwani
A model is
built using
the causes
and symp-
toms of
PCOS as in-
puts and the
output is
predicted as
presence or
absence or
PCOS.
Machine
learning su-
pervised clas-
sification al-
gorithms
used are K-
NN and Lo-
gistic Regres-
sion.
The best ac-
curate model
built is Lo-
gistic Regres-
sion with
accuracy
92%.
Madhumith
a et al.
[2021]
Ovary
details are
large range
of follicles,
type of cysts,
follicle size,
using image
segmenta-
tion
Based on pre-
processing
and morpho-
logical opera-
tions SVM,
KNN and Lo-
gistic Regres-
sion were
used.
All three al-
gorithms
were com-
bined and
hybrid model
were made
and 0.98 ac-
curacy were
achieved
Pijush et al.
[2021]
Detection
and preven-
tion of this
disease as
early as pos-
sible.
Used SMOTE
and five other
algorithms
such as Lo-
gistic Regres-
sion, Random
Forest, Deci-
sion Tree
Support vec-
tor machine
and K-NN
together for
early detec-
tion of PCOS.
The best
model
achieved ac-
curacy,
Training
time: 97.11,
F1 score:
0.010sec,
Recall: 98%,
Precision:
98% and
AUROC:
95.6%
Khan Inan
et al. [2021]
Conducting
a probabilis-
tic approach
to select
statistically
relevant
features
which con-
tribute to
PCOS in-
stances.
SMOTE, ENN
and ANOVA
Test, Chi-
Square Test
were used to
identify im-
portant fea-
tures. Classi-
fiers such as
XG Boost,
SVM, KNN,
NB, MLP, RF,
AdaB were
used.
G Boost out-
performedall
other classi-
fiers with
0.96 accuracy
and 0.98 Re-
call.
2. Methodology
Development of machine learning model to train the dataset
is an important step for successful implementation. The da-
taset contains attributes such as I beta-HCG (mIU/mL), II
beta-HCG (mIU/mL), AMH (ng/mL), Age(yrs), Weight (Kg),

Height (Cm), BMI, Blood Group, Pulse rate(bpm), RR
(breaths/min), Fast food, Reg Exercise,BP_Systolic (mmHg),
etc.
Fig -1: Block diagram of the system
1. Defining the Problem
The first most important step in is to define the problem
by including the inputs provided in the model and the
expected output of the model.
2. Data Collection
This is the crucial step of collecting data. How good the
model will perform, the accuracy that we will get de-
pends on the dataset. We can collect data from various
platforms such as Kaggle, UCI Repository, BuzzFeed
News, etc. We performed research on the dataset ob-
tained on Kaggle named Polycystic ovary syndrome
(PCOS).
3. Selection of implementation Platform
For this machine learning implementation, the platform
used is Jupyter Notebook, language used – Python.
4. Data Preparation
When appropriate data is identified, the data should be
shaped in order to train the model. The data obtained
will be in csv format in python. Visualize the data and
check the correlations between different characteristics.
In this step checking for missing values or incomplete
records, aggregation,augmentation,normalization,label-
ling, structured, unstructured and semi- structured data
these activities are performed. This dataset contains the
women patients in which they are suffering from PCOS.
Further the steps to be performed in Data Preprocessing
are:
i. Data Cleaning
It is the process of identifying the incorrect, incomplete
or missing part of the data and then modifying, replacing
or deleting them. In our paper the dataset was checked
for missing values first by using the PandasandNumpy2.
ii. Data Labeling
It is a method of identifying raw data i.e videos,
images, text files, etc and add informative tags to provide
context to increase the significance of machine learning
model. The non-numerical are transformed into
numerical values.
iii. Feature Selection
Feature Selection is an important step in which most
relevant features are extracted from the datasetandthen
machine learning algorithms are applied for the better
performance of model. It has a goal to find the best
possible features for building the model ignoring the
irrelevant details. Feature selection can be performed
with common techniques including Filter methods,
Wrapper methods and Embedded methods.
2.1 Modelling
When the data is completely cleaned and selected, it is
ready to be processed by the algorithms. The algorithms
used to create the model are Random Forest, Decision
Tree, Support Vector Classifier, Logistic Regression, K-
Nearest neighbors, XGBRF, CatBoost Classifier.
Random Forest
Random Forest is a kind of supervised machine learning
algorithm used for both Classification and Regression. Its
builds multiple decision trees and merges them together
to get a more accurate and stable prediction.
Decision Tree
Decision Tree is of type supervised learning algorithm,
graphically represented for getting all possible solutions
to a problem based on given conditions. It used CART al-
gorithm which stands for Classification and Regression
Tree algorithm.
Support Vector Classifier (SVC)
SVC is to fit the data provided returning a best fit that di-
vides or categorizes the data. The data points are closer
to the hyperplane and causes change in position and ori-
entation of the given hyperplane.
Logistic Regression
Logistic Regression is a type of supervisedLearningtech-
nique used for solving the classification problems. It is a

machine learning algorithm used for predicting the cate-
gorical dependent variable using a given set ofindepend-
ent variables and the cost function is limited between
values 0 and 1.
K Nearest Neighbor (KNN)
K Nearest Neighbor also known as lazy learneralgorithm
is a supervised Learning algorithms used for both classi-
fication and regression. Instead of instantly learning the
dataset, it first stores the dataset and then at the time of
classification performs action on the given dataset.
XGBRF
XGBoost with Random Forest (XGBRF) is an ensemble
method used for classification of PCOS. XGBoost is a gra-
dient boosting algorithm and Random Forest is an exam-
ple of bagging algorithm. XGBRF is a modified version of
XGBoost classifier. The advantage of XGBRF is it is used
to overcome the problem of over-fitting.
CatBoost Classifier
Categorical Boosting CatBoost or is an open-source
boosting library used for regression and classification.It
works with multiple categories of data, including audio,
text and image including historical data. The technique
used in this algorithm is to perform conversion from cat-
egorical values into numbers using different types ofsta-
tistics on combinations of categorical features and com-
binations of categorical and numerical features.
Cross Validation
Cross-Validation in machine learning is a technique for
validating the model efficiency in which model is trained
using the subset of the dataset and after training the
model is evaluated using the complementary subset of
the dataset.
Fig -2: Correlation between Features
Results and Discussion
The experimentation is performed on the dataset using
various machine learning algorithms. The mainobjective
is to find most suitable algorithm for the classification of
the dataset created. The algorithms used to constructthe
model are Decision Tree, SVC, Random Forest, Logistic
Regression, K Nearest Neighbor, XGBRF and CatBoost
Classifier.
Table -1: Accuracy of Different Classifier Models
Models Accuracy
Decision Tree 82.79
SVC 69.05
Random Forest 89.42
Logistic Regression 83.32
K Nearest Neighbors 74.34
XGBRF 85.89
CatBoost Classifier 92.64

Fig -3: Accuracy of different Classifier Models
The accuracies obtained by different algorithms are: Deci-
sion Tree – 82.79%, SVC – 69.05%,RandomForest– 89.42%,
Logistic Regression – 83.32%, K-Nearest Neighbors –
74.34%, XBRF – 85.89%, CatBoostClassifier – 92.64%.
Therefore, from the above results, conclusion is CatBoost-
Classifier has outperformed and obtained highest accuracy.
3. CONCLUSIONS
In this paper, Machine Learning model is successfully built
and trained for early detection of PCOS. PCOS is one of the
very common condition in women associated with psycho-
logical, reproductive and metabolic features. Some day-to-
day activities to decrease the effects of PCOS are maintain a
healthy weight, limit carbohydrates, be active, exercisedaily
and eat healthy food. The system in this paper helps in early
detection of PCOS from an optimal and minimal set of pa-
rameters which have been statistically analyzed. Among the
various algorithmsusedCatBoostClassifierisfoundsuperior
in performance. This model can be used by doctors for early
screening and diagnosing patients who are likely to develop
this disorder. Therefore, with the use of various machine
learning techniques we have built a model to detect PCOS at
an early stage.
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