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PCOS Detect using Machine Learning Algorithms
1.
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 ---------------------------------------------------------------------***---------------------------------------------------------------------- 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
2.
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 1215 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),
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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 1216 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
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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 1217 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
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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 1218 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. REFERENCES [1] Palak Mehrotra, Jyotirmoy, Chatterjee, Chandan Chakraborty, “AutomatedScreeningofPolycystic Ovary Syndrome using Machine Learning Techniques”, IEEE, 2012. [2] Bedy Purnama, Untari Novia Wisesti, Adiwijaya,Fhira Nhita, Andini Gayatri, Titik Mutiah, “A Classification of Polycystic Ovary Syndrome Based on FollicleDetec-tion of Ultrasound Images, 2015 3rd International Confer- ence on Information and Communication Tech- nology (ICoICT). [3] Amsy Denny, Anita Raj, Ashi Ashok, Maneesh Ram C, Remya George, “i-HOPE: Detection And Prediction Sys- tem For Polycystic Ovary Syndrome (PCOS) Using Ma- chine Learning Techniques”, 2019 IEEE Region 10 Con- ference (TENCON 2019). [4] Subrato Bharati, Prajoy Podder, M. Rubaiyat Hossain Mondal, “Diagnosis of Polycystic OvarySyndromeUs- ing Machine Learning Algorithms”. 2020 IEEE Region 10 Symposium (TENSYMP), 5-7 June 2020, Dhaka, Bangladesh. [5] Ning-Ning Xie, Fang-Fang Wang, Jue Zhou, Chang Liu, Fan Qu, “Establishment and Analysis of a Combined Diagnostic Model of Polycystic Ovary Syndrome with Random Forest and Artificial Neural Network”, Hindawi BioMed Research International Volume 2020. [6] Priyanka R. Lele, Anuradha D. Thakare, “Comparative Analysis of Classifiers for Polycystic OvarySyndromeDe- tection using Various Statistical Measures”,International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181: Vol. 9 Issue 03, March-2020. [7] Namrata Tanwani, “Detecting PCOS using Machine Learning”, IJMTES | International Journal of Modern Trends in Engineering and Science ISSN: 2348-3121, Volume:07 Issue:01 2020. [8] J. Madhumitha, M. Kalaiyarasi, S. Sakthiya Ram, “Au- tomated Polycystic Ovarian Syndrome Identification with Follicle Recognition”, 2021 3rd International Con- ference on Signal Processing and Communication [9] Pijush Dutta, Shobhandeb Paul, Madhurima Majum- der, “An Efficient SMOTE Based Machine Learning classification for Prediction & Detection of PCOS”,Re- search Square, November 8th, 2021. [10] Muhammad Sakib Khan Inan, Rubaiath E Ulfath, Fahim Irfan Alam, Fateha Khanam Bappee, Rizwan Hasan, “Improved Sampling and Feature Selection to Support Extreme Gradient Boosting forPCOSDiagno- sis.
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