International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 06 | June 2022
p-ISSN: 2395-0072
www.irjet.net
PREDICTING THE RISK OF HAVING HEART DISEASE USING MACHINE LEARNING TECHNIQUES Chandu D1, Ch Sivakumar2, Darshan Vinayak S3, Dereddy Parthasaradhi Reddy4, Karthik M D5 1,2,3,4,5Student, Dept. of Computer science and Engineering, Dayananda Sagar University, Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------Machine Learning helps in predicting heart diseases, and Abstract - Machine Learning is employed across many
therefore the predictions made are quite accurate.
spheres round the world. The healthcare industry isn't any exception. Machine Learning can play a vital role in predicting the presence/absence of Locomotor disorders, heart diseases, and more. the target of the proposed model is to predict the danger of getting cardiopathy using machine learning technics. Machine learning is widely used nowadays in many business applications like e-commerce and plenty of more. Prediction is one amongst the areas where this machine learning is employed, our topic is about the prediction of cardiopathy by processing patient datasets and data of patients to whom we want to predict the possibility of occurrence of cardiovascular disease. Such information, if predicted well before, can provide important insights to doctors who can then adapt their diagnosis and treatment per-patient basis.
We can train our prediction model by analyzing existing data because we already know whether each patient has cardiopathy. This process is additionally referred to as supervision and learning. The trained model is then accustomed predict if users suffer from heart condition.
2. PROPOSED SYSTEM Our system is a website-based machine learning application trained on a dataset from Kaggle. The admin inputs the required attributes to get the prediction for that patient. The model will determine the probability of heart disease. We have tested the following sixteen algorithms:
Key Words: Heart disease prediction, Classification, Regression, Machine learning and
2.1 CLASSIFICATION ALGORITHMS
1. INTRODUCTION
1. Logistic Regression 2. Naive Bayes
The objective of the proposed model is to predict the chance of getting heart condition using machine learning techniques. Machine learning is widely used nowadays in many business applications like e-commerce and lots of more. Prediction is one in all the areas where this machine learning is employed, our topic is about the prediction of cardiopathy by processing a patient’s dataset and data of patients to whom we want to predict the prospect of occurrence of heart condition.
3. Support Vector Machine 4. K-Nearest Neighbors 5. Decision Tree 6. XG Boost
2.2 REGRESSION ALGORITHMS
Heart disease could be a term covering any disorder of the guts. Heart diseases became a significant concern to handle as studies show that the amount of deaths thanks to heart diseases has increased significantly over the past few decades in India, in fact, it's become the leading reason behind death in India. A study shows that from 1990 to 2016 the death rate thanks to heart diseases increased by 34 percent from 155.7 to 209.1 deaths per one lakh population in India. Thus, preventing heart diseases has become quite necessary. Good data-driven systems for predicting heart diseases can improve the whole research and prevention process, ensuring that more people can live healthy lives. this can be where Machine Learning comes into play.
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1.
Logistic Regression
2.
Polynomial Regression
3.
Support Vector Regression with RBF kernel
4.
Support Vector Regression with Linear kernel
5.
Support Vector Regression with Poly kernel
6.
Decision Tree Regression
7.
Bayesian Ridge
8.
Lasso
9.
Ridge
10. Random Forest Regression
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