Author(s):
1. Mohsin Mahmood:
Transport and Mass Transit Department, Government of KPK Peshawar, Pakistan
2. Aqib Mehmood:
Transport and Mass Transit Department, Government of KPK Peshawar, Pakistan
Abstract:
The worldwide prevalence of heart disease is on the rise, emphasizing the urgency for early detection methods and preventive actions. This research investigates the feasibility of employing different statistical models to anticipate the onset of heart disease by examining crucial health markers and lifestyle variables. By leveraging data from the University College Irvine (UCI) Dataset, which includes comprehensive patient attributes, researchers aim to develop a predictive framework. Three distinct Machine Learning Classifier Models – Logistic Regression, K-Nearest Neighbors, and Random Forest – are employed to construct this predictive model. Initial sections of the paper focus on identifying crucial clinical features associated with heart disease, while subsequent segments evaluate the accuracy of the predictive models on the dataset. Through this approach, the study aims to contribute to the early diagnosis and prevention of cardiovascular diseases, utilizing advanced data mining and machine learning techniques.
Page(s):
1-1
DOI:
DOI not available
Published:
Journal: Second International Conference on Computing Technologies, Tools and Applications (ICTAPP-24), June 4-6,2024 (Abstract Book), Volume: 0, Issue: 0, Year: 2024
Keywords:
Logistic regression
,
machine learning
,
KNN
,
Random Forest
,
CNN
,
Prediction
,
Heart disease
,
Encompass Data Mining
References:
References are not available for this document.
Citations
Citations are not available for this document.