Atherosclerosis Disease Prediction Based on Feature Optimization and Ensemble Classifier

2022 ◽  
pp. 399-411
Author(s):  
Brajesh Kumar ◽  
Harsh Mathur
Keyword(s):  
Ensemble Classifier ◽  
Disease Prediction ◽  
Feature Optimization

scholarly journals Heart Disease Prediction using Ensemble Learning Method

2020 ◽  
Vol 9 (1) ◽  
pp. 2612-2616
Keyword(s):  
Heart Disease ◽  
Ensemble Classifier ◽  
World Health ◽  
Disease Prediction ◽  
Decision Tree Algorithm ◽  
Unhealthy Lifestyle ◽  
The World ◽  
History Of ◽  
Efficient Prediction ◽  
Health Organization

The human heart is the very important organ in our body. The World Health Organization estimates 31% of deaths are due to heart disease taking an estimated 1.79 crore lives. Unhealthy lifestyle, family history of heart problems, stress, etc. are few risk factors for heart disease. In this paper we are proposing an ensembling classifier using K-NN[17] , SVM[18], MK-NN and CART[19] (Decision Tree algorithm) for the efficient prediction of heart disease. The performance and efficiency of the algorithms and ensembling classifier are evaluated. The results indicate that the proposed system was more accurate to determine the existence or non-existence of heart disease. Out of these algorithms, ensemble classifier predicts heart disease more accurately. The accuracy is above 93%.

Feature optimization by discrete weights for heart disease prediction using supervised learning

2020 ◽  
Author(s):  
Fuad Ali Mohammed Al-Yarimi ◽  
Nabil Mohammed Ali Munassar ◽  
Mohammed Hasan Mohammed Bamashmos ◽  
Mohammed Yousef Salem Ali
Keyword(s):  
Heart Disease ◽  
Supervised Learning ◽  
Disease Prediction ◽  
Feature Optimization

scholarly journals Study of Ensemble Classifier for Prediction in Health Care Data

2019 ◽  
Vol 8 (S1) ◽  
pp. 36-37
Author(s):  
S. Sathurthi ◽  
R. Kamalakannan ◽  
T. Rameshkumar
Keyword(s):  
Health Care ◽  
Care Facility ◽  
Health Care Facility ◽  
Good Deal ◽  
Ensemble Classifier ◽  
Disease Prediction ◽  
Health Record ◽  
Health Care Data ◽  
Quality Of Patient Care

Electronic health record systems are adapted in a good deal of health care facility to improve the quality of patient care which is maintained electronically. Developing a disease prediction model for health care system can help us to overcome the problem of medical distress. In this study, we suggest ensemble technology and statistical methods to search through massive amounts of information, analyzing it to predict outcomes for individual patients. Using Weka tool, breast-cancer and diabetes medical datasets have experimented with ensemble classifier.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Sign in / Sign up

Export Citation Format

Share Document