scholarly journals Heart Attack Prediction by using Machine Learning Techniques

2020 ◽  
Vol 8 (5) ◽  
pp. 1577-1580
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Comparative Analysis ◽  
Missing Data ◽  
Heart Attack ◽  
Performance Metrics ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Light Weight ◽  
Learning Techniques

Heart disease is most common now a days and it is a very serious problem. Machine learning provides a best way for predicting heart disease. The aim of this paper is to develop simple, light weight approach for detecting heart disease by machine learning techniques. Machine learning can be implemented in heart disease prediction. In this paper different machine learning techniques have been used and it compares the result using various performance metrics. This study aims to perform comparative analysis of heart disease detection using publicly available dataset collected from UCI machine learning repository. There are various datasets available such as Switzerland dataset, Hungarian dataset and Cleveland dataset. Here Cleveland dataset is used which is having 303 records of patients along with 14 attributes are used for this study and testing. These datasets are preprocessed by removing all the noisy and missing data from the dataset. And then the preprocessed dataset are used for analysis. In this study six different machine learning techniques were used for comparison based on various performance metrics. The analysis shows that out of six techniques SVM gives the best result with 89.34%. A GUI is developed for the prediction of heart disease.

scholarly journals Heart Disease Prediction using Machine Learning Techniques

2021 ◽  
pp. 42-47
Author(s):  
Ramesh Ponnala ◽  
K. Sai Sowjanya
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Linear Model ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Huge Amount ◽  
Healthcare Enterprise ◽  
Learning Techniques ◽  
Accuracy Level

Prediction of Cardiovascular ailment is an important task inside the vicinity of clinical facts evaluation. Machine learning knowledge of has been proven to be effective in helping in making selections and predicting from the huge amount of facts produced by using the healthcare enterprise. on this paper, we advocate a unique technique that pursuits via finding good sized functions by means of applying ML strategies ensuing in improving the accuracy inside the prediction of heart ailment. The severity of the heart disease is classified primarily based on diverse methods like KNN, choice timber and so on. The prediction version is added with special combos of capabilities and several known classification techniques. We produce a stronger performance level with an accuracy level of a 100% through the prediction version for heart ailment with the Hybrid Random forest area with a linear model (HRFLM).

scholarly journals Heart Disease Prediction using Machine Learning

2020 ◽  
Vol 9 (1) ◽  
pp. 700-704
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Nearest Neighbour ◽  
Decision Tree Classifier ◽  
Support Vector Classifier ◽  
Learning Techniques ◽  
Tree Classifier

Deriving the methodologies to detect heart issues at an earlier stage and intimating the patient to improve their health. To resolve this problem, we will use Machine Learning techniques to predict the incidence at an earlier stage. We have a tendency to use sure parameters like age, sex, height, weight, case history, smoking and alcohol consumption and test like pressure ,cholesterol, diabetes, ECG, ECHO for prediction. In machine learning there are many algorithms which will be used to solve this issue. The algorithms include K-Nearest Neighbour, Support vector classifier, decision tree classifier, logistic regression and Random Forest classifier. Using these parameters and algorithms we need to predict whether or not the patient has heart disease or not and recommend the patient to improve his/her health.

scholarly journals Heart Disease Prediction using Machine Learning Techniques

2020 ◽  
Vol 1 (6) ◽  
Author(s):  
Devansh Shah ◽  
Samir Patel ◽  
Santosh Kumar Bharti
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Learning Techniques

scholarly journals Heart Disease Prediction Using Machine Learning Algorithms

2020 ◽  
pp. 137-149
Author(s):  
Abhay Agrahary
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Heart Disease ◽  
Accurate Diagnosis ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Huge Amount ◽  
Learning Techniques ◽  
Comparative Results

Heart disease is one of the most fatal problems in the whole world, which cannot be seen with a naked eye and comes instantly when its limitations are reached. Therefore, it needs accurate diagnosis at accurate time. Health care industry produced huge amount of data every day related to patients and diseases. However, this data is not used efficiently by the researchers and practitioners. Today healthcare industry is rich in data however poor in knowledge. There are various data mining and machine learning techniques and tools available to extract effective knowledge from databases and to use this knowledge for more accurate diagnosis and decision making. Increasing research on heart disease predicting systems, it become significant to summarize the completely incomplete research on it. The main objective of this research paper is to summarize the recent research with comparative results that has been done on heart disease prediction and also make analytical conclusions. From the study, it is observed Naive Bayes with Genetic algorithm; Decision Trees and Artificial Neural Networks techniques improve the accuracy of the heart disease prediction system in different scenarios. In this paper commonly used data mining and machine learning techniques and their complexities are summarized.

Heart Disease Prediction using Machine Learning Techniques

2021 ◽  
Author(s):  
Reldean Williams ◽  
Thokozani Shongwe ◽  
Ali N. Hasan ◽  
Vikash Rameshar
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Learning Techniques
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