Conference PaperPDF Available

Intelligent Heart Disease Prediction system using Integration of Genetic Algorithm and Naïve Bayes

June 2024

June 2024

Authors:

Dr Ralla Suresh

ACE Engineering College

Content uploaded by Dr Ralla Suresh

Content may be subject to copyright.

ResearchGate has not been able to resolve any citations for this publication.

ResearchGate has not been able to resolve any references for this publication.

Article

Full-text available

ENSEMBLE HEART DISEASE PREDICTION USING ENHANCED FEATURE SELECTION BY GENETIC ALGORITHM AND BEST ACC...

December 2021

Dr Ralla Suresh

A doctor's competence and experience in the relevant field is used to make a clinical diagnosis for any ailment. Lack of experience in the relevant topic has even resulted in incorrect diagnosis and treatment. Patients must undergo a number of tests in order to be diagnosed with heart disease. Not every test that is routinely performed is essential for accurate illness diagnosis.The goal of the ... [Show full abstract] study is to find the presence or absence of heart disease based on reduced number of attributes. In this research, a genetic algorithm is used to choose features from 76 features in a heart disease dataset gathered from multiple hospitals, as well as features from the Cleveland health dataset retrieved from the UCI repository. A genetic algorithm selects the best features that lead to accurate prediction. This minimizes the number of tests required, lowering treatment costs. Using genetic feature reduction, seventy-six traits are reduced to sixteen. Three classifiers, such as SVM, Naive Bayes, and KNN, classifiers used for predicting the heart disease with approximately similar accuracy if the no of attributes are lessened. After the attributes were minimized using a genetic approach, Naive Bayes fared better, regularly scoring 99.68 percent. Without feature reduction, classification accuracy was poor, and treatment costs were higher.

View full-text

Article

Full-text available

GENETIC FEATURE SELECTION AND NAIVE BAYES FOR EFFICIENT HEART DISEASE PREDICTION

June 2023

Dr Ralla Suresh

Heart disease is the leading cause of death in the world and it is easier to treat when detected early. The data in the health sector are huge but have not been used potentially because of its complexity in the system. The main reason for the complexity is the lack of adequate data analysis tools in the key patterns. Machine learning can help in the retrieval of useful information from existing ... [Show full abstract] data and it also helps in the training of a model to forecast patients' health, which is faster than clinical experimentation. The Cleveland heart datasets have been used in a lot of studies. The existing K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression, Naive Bayes, and other classifiers were used to limit the number of selected attributes. The present research applied Genetic algorithm (GN) modeling to discover the 16 related features of the heart data set from hospitals, in India. The proposed model showed transparent and reliable graphical representation with other attributes which has the ability to predict the disease. The proposed model obtained 95 % accuracy better than existing models.

View full-text

Poster

Full-text available