=Paper=
{{Paper
|id=Vol-3058/paper40
|storemode=property
|title=Machine Learning Based Approach Using Xgboost For Heart Stroke Prediction
|pdfUrl=https://ceur-ws.org/Vol-3058/Paper-065.pdf
|volume=Vol-3058
|authors=Sukhmanjot Dhillon,Chirag Bansal,Brahmaleen Sidhu
}}
==Machine Learning Based Approach Using Xgboost For Heart Stroke Prediction==
https://ceur-ws.org/Vol-3058/Paper-065.pdf
Machine Learning Based Approach Using XGboost for Heart
Stroke Prediction
Sukhmanjot Dhillon1, Chirag Bansal2 and Brahmaleen Sidhu3
1,2,3
Department of Computer Science and Engineering, Punjabi University Patiala, Punjab
Abstract
Many prediction methods are widely used in clinical decision-making to predict the prevalence of
diseases, assess the prognosis or outcome of diseases, and help doctors treat diseases. However,
traditional predictive models or methods are not enough to effectively collect basic data because
they cannot simulate the quality of mapping the negative attributes of the medical field. The
approach proposed in this paper uses. We use machine learning to predict survival of a heart
patient. The approach uses patient’s data like gender, age, hypertension, type of work, glucose
level, body mass index, etc. to predict his/her chances of death due to heart failure. The dataset is
retrieved from Kaggle. Machine learning based classification algorithms namely XGboost,
Random Forest, Navies Bayes, Logistic Regression and Decision Tree have been implemented
and their performance has been compared using parameters like precision, recall, F1-score and
AUC.
Keywords 1
Machine learning, Stroke, Risk level classification, XGboost
1. Introduction
Heart diseases have seriously affected the world. Coronary artery disease is a common kind of
heart disease. It is caused by buildup of plaque in the walls of the coronary corridors. Coronary
corridors are answerable for providing blood to the heart and other body organs.Normal indications of
the coronary illness are chest torment and distress. In some cases, heart attack is the first sign of the
disease. This is accompanied by weakness, light-headedness, nausea, cold sweat, pain in the arms, and
shortness of breath. The main causes of this disease are family history of disease, excess body weight,
lack of activity, unhealthy eating, use of tobaccoetc.If not treated well in time heart disease can cause
heart failure leading to death of patient.
In case of heart diseases, prevention is definitely better than cure. An early warning can be
beneficial in saving the life of the patient. A data based system that provides timely indication of the
risk of heart failure and is supported by medical information from patient’s health data can be
revolutionary. Great development has been achieved in the field of clinical and medical services using
artificial intelligence, machine learning and data science approaches. Joining sensors with specialized
gadgets can assist patients with getting input from all points, regardless of whether they are doing
what they are doing. As of late, medical services has moved from the facility level to the patient-
International Conference on Emerging Technologies: AI, IoT, and CPS for Science & Technology Applications, September 06–07, 2021,
NITTTR Chandigarh, India
EMAIL: banidhillon1@gmail.com (A. 1); chiragbansal254@gmail.com (A. 2); brahmaleen.sidhu@gmail.com (A. 3)
ORCID: Not Available (A. 1); Not Available (A. 2); 0000-0001-6519-7957 (A. 3)
©2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
driven level .In this speedy world, it is not difficult to direct a naturally directed person wellbeing test
to get any individual in the tempest before a respiratory failure
The approach proposed in this paper uses machine learning to predict survival of a heart patient.
The approach uses patient’s data like gender, age, hypertension, type of work, glucoselevel, body
mass index, etc. to predict his/her chances of death due to heart failure. The dataset is retrieved from
Kaggle ..AI based grouping calculations specifically XGboost , Random timberland , Navies Bayes ,
Logistic Regression and Decision Tree have been implemented and their performance has been
compared using parameters like precision, recall, F1-score and AUC.
2. Related Work
The forms of machine learning used to predict heart attacks are very useful and have proven their
importance in recent years. Manasa, Gupta[1] received a system that can be used to predict recurrent
cardiovascular disease what's more, can be utilized by clients with coronary conduit infection. They
utilized the Random Forest calculation which gave an exactness of 89%.
Rajliwall, et.al.[2] In this document, you need to plan a framework that supports supervised
learning algorithms and package-level processes that use group isolation and channels dependent on
sex, training, and age. Sentil Kumar Mohan[3], ChandrasgarTirumalai, GautamSrivachava. Utilize the
mixture HRFLM strategy, which consolidates the elements of irregular timberland (RF) and straight
technique (LM).
Nashif, Raihan,[4] A model is proposed, which might be a cloud-based coronary illness
expectation model, which means to utilize AI calculations to recognize the following kind of coronary
illness. Susmitha Manikandan conveys a twofold grouping model in the example paper module of this
framework, which is utilized to foresee patients' irregular issues dependent on the patient's clinical
information. They utilized a Random Forest With Linear Model that gave a precision of 88.7%.
Gavhane, et.al.[4] In this article, they designed a system in which they used NN formulas and
hierarchical perceptrons to train and test data sets. Ravish, K. Shanti, Nayana R. Shenoy, S.Nisarg,
and ECG data. Teach artificial neural organizations to precisely analyze and anticipate heart
irregularities (assuming any). Utilizing the innocent Bayes strategy, calling the tree, K-closest
neighbor, and arbitrary backwoods in 10-crease cross-approval, the exactness rate comes to 80%
Jae Woo Lee[5], The purpose of this article is to calculate and predict the probability of stroke
within 10 years: "Computer strategies and procedures in biomedicine" Lee, Hyungsun Lim,
et.al.Individual 5 Stroke-like probability
Stroke Probability[6]: A Risk Profile of the Framingham Study, Wolff,et.al. In this article, a health
risk assessment function was developed to predict the incidence of stroke in the Framingham study
cohort.
Formulate rules to assist sisters in predicting stroke[7]: National Health Insurance Information
Survey Min SN, Pak SJ, Kim JJ, Subramaniyam M, Lee KS. -The purpose of this research is to derive
the model equations used to develop preliminary recognition algorithms. Stroke with risk factors that
may change.
3. Proposed Work
In this article, we have fostered a model that contains a double order of life and a sign of the
danger of cardiovascular breakdown and is upheld by clinical data from individual information. The
informational index we use comes from the machine Kaggle. Unstructured informational indexes are
renewed in organized informational collections. The informational index has twelve ascribes, eleven
of which are indicators. One chance is a double reaction variable. The outrageous incline of the slope
is utilized in the order cycle. Calculations Involved-Few approaches utilized in our activities are:
Decision Tree: It is a choice help device that utilizes a tree-like chart or model of choices and their
potential results, including chance occasion results, asset expenses, and utility. It is one approach to
show a calculation that just contains restrictive control articulation
Naïve Bayes: It is a probabilistic machine-learning model that’s used for classification tasks. The
crux of the classifier is based on the Bayes theorem.
XGboost: XGboost is a good implementation of the gradient enhancement method. Even though
there may not be new mathematical developments here, it is a gradient gain alternative that can be
carefully designed for optimization and accuracy. It consists of a linear version, and the newborn tree
may be a technique that uses various AI calculations to verify whether a fragile newbie will create a
reliable newbie to improve the accuracy of the version. From (impulsive) and parallel learning
(bagging), for example, random forest. Data collection can be a method that can be used to control the
display of an AI version with advanced talent and precision processing is faster than enhancing
gradients. These are built-in methods for closing the data gap.
Figure 1: Proposed Methodology
4. Implementation
Figure 2: Steps of Implementation
4.1. Data Preprocessing
After collecting multiple files, process the information. This data set contains a large number of
patient records. A total of 5110 + 43400 = 48510 files. 1663 The file is missing some values. The
remaining 46,847 records are used for preprocessing. The factors of the informational collection
boundaries are prepared. This variable can be utilized to check whether an individual has an
extra/diminished danger of a respiratory failure. A cardiovascular failure is in progress, the worth is
set to one (1), else, it is zero (0). The outcomes show that 37 of the 297 records have a worth of 1
demonstrating the commonness of focal dead tissue, and the excess 160 segments have a worth of 0,
which is more averse to cause a coronary failure. The accompanying boundaries are remembered for
the last mathematical informational index. The information record is in CSV design. There are twelve
boundaries altogether recorded in underneath table:
Table 1
Dataset features
Feature Name Description
id Unique identification number
gender Male or Female
age Age of the patient
hypertension Presence: 0 Absence: 1
heart_disease Presence: 0 Absence: 1
ever_married Yes or No
work_type Children, Government job, Neverworked, Private
sector job or Self-employed
Residence_type Rural or Urban
avg_glucose_level Patient’s level of glucose
BMI Body Mass Index of patient
smoking_status Smoked formerly, never smoked, smokes or
unknown
Stroke 0 or 1
4.2. Feature Selection and Reduction
Two of the twelve boundaries are utilized to characterize patient information. 10 inverse
boundaries are required. These ten boundaries are basic to the extraordinary and definitive condition
of the heart. During the analysis, different types of AI were found, particularly basic numerical
strategies like KNN, SVM, XGboost, and irregular timberland. Rehash the test by blunder taking care
of many AI techniques with similar properties.
4.3. Classification and Modeling
Since our informational collection is prepared, many AI methodologies can be applied. Any place
characterization results are acquired, numerous calculations are chosen, and their presentation is
thought about, arrangement and recreation are a significant piece of the framework. In this load of
calculations, XGboost gives us exceptionally precise outcomes.
5. Learning Method:
XGboost
The level of formula rule development performance includes accuracy, search, F measurement, and
level accuracy. Such metrics are evaluated based on real transaction prices (TP), real negative values
(TN), false-positive values (FP), and false negative values. (FN)
Accuracy 𝑇𝑇𝑇𝑇 (1)
𝑃𝑃 =
𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹
Recall 𝑇𝑇𝑇𝑇 (2)
𝑅𝑅 =
𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹
F-Measure 2𝑃𝑃𝑃𝑃 (3)
𝐹𝐹 =
𝑃𝑃 + 𝑅𝑅
The Total Accuracy 𝑇𝑇𝑇𝑇 + 𝑇𝑇𝑇𝑇 (4)
𝐹𝐹 =
𝑇𝑇𝑇𝑇 + 𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹 + 𝐹𝐹𝐹𝐹
Table 2
Evaluated values
XGboost Random Navies Bayes Logistic Decision Tree
Forest Regression
Precision (%) 81.50% 51.34% 66.96% 31.54% 33.18%
Recall (%) 87.00% 92.81% 94.99% 94.52% 95.55%
F1-score (%) 95.20% 66.11% 78.55% 47.30% 49.26%
AUC (%) 97.48% 82.52% 96.64% 71.85% 71.15%
6. Conclusion
This paper presents the execution and correlation of AI based arrangement methods specifically
XGboost, Random Forest, Navies Bayes, Logistic Regression and Decision Tree to foresee endurance
of a heart patient. The dataset used contains information like patient’s gender, age, hypertension, type
of work, glucose level, body mass index, etc. to predict his/her chances of death due to heart failure.
The performance of the algorithms has been compared using parameters like precision, recall, F1-
score and AUC. By the usage of XG Boost, an accuracy of 97.56% is obtained.
7. References
[1] K. N. Manasa, PrinceKumarGupta,DiseasePredictionbyMachineLearningwiththe help of Big
Data from Healthcare Communities, International Journal of Engineering Science And
Computing (2017).
[2] Nitten S. Rajliwall, Rachel Davey, GirijaChetty. (2018), Cardiovascular Risk Prediction Using
XGBoost. Institute of Electrical and Electronics Engineers (IEEE).
[3] SenthilKumar Mohan, ChandrasegarThirumalai, Gautam Srivastava. (2019). Effective Heart
Disease Prediction Using Hybrid Machine Learning Techniques, Institute of Electrical and
Electronics Engineers (IEEE).
[4] ShadmanNashif, Md. RakibRaihan (2018), Heart Disease Detection by Machine Learning
Algorithms and Real-Time Cardiovascular Health Monitoring System, World Journal of
Engineering and Technology.
[5] “Computer Methods and Programs in the Biomedicine” - Jae–woo Lee, Hyun-sun Lim, Dong-
wook Kim, Soon-ae Shin, Jinkwon Kim, Bora Yoo, Kyung-hee Cho
[6] “Probability of Stroke: A RiskProfile from the Framingham Study” -Philip A.Wolf, MD;
Ralph B. D’Agostino, PhD, Albert J. Belanger, MA; and William B.Kannel
[7] “Development of an Algorithm for Stroke Prediction: A National Health Insurance Database
Study” - Min SN, Park SJ, Kim DJ, Subramaniyam M, Lee KS