Using Machine Learning Technologies to Classify and Predict Heart Disease
Küçük Resim Yok
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Science and Information Organization
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The techniques of data mining are used widely in the healthcare sector to predict and diagnose various diseases. Diagnosis of heart disease is considered as one of the very important applications of these systems. Data is being collected today in a large amount where people need to rely on the device. In recent years, heart disease has increased excessively and heart disease has become one of the deadliest diseases in many countries. Most data sets often suffer from extreme values that reduce the accuracy percentage in classification. Extreme values are defined in terms of irrelevant or incorrect data, missing values, and the incorrect values of the dataset. Data conversion is another very important way to preconfigure the process of converting data into suitable mining models by acting assembly or assembly and filtering methods such as eliminating duplicate features by using the link and one of the wrap methods, and applying the repeated discrimination feature. This process is performed, dealing with lost values through the "Remove with values" methods and methods of estimating the layer. Classification methods like Naïve Bayes (NB) and Random Forest (RF) are applied to the original datasets and data sets with the feature of selection methods too. All of these operations are implemented on three various sets of heart disease data for the analysis of pre-treatment effect in terms of accuracy. © 2021
Açıklama
Anahtar Kelimeler
(Support Vector Machine SVM), Classification, machine learning, Naive Bayes (NB), Random Forest
Kaynak
International Journal of Advanced Computer Science and Applications
WoS Q Değeri
Scopus Q Değeri
Q3
Cilt
12
Sayı
3
