Volume 11 - Volume 11
Automatic Verbal Autopsy Classification Using Multinomial Logistic Regression Classifier by Using Recursive Feature Elimination
Abstract
Verbal autopsy is one of the finest medical process to identify automatically the cause of a death
afore medical ascendant entities will certify it. Identifying the exact cause is intricate and fuzzy in
nature. The dataset with an exact cause of death is a paramount implement for every country to make
the presage about the life style and medical facilities available to the people. Multinomial logistic
regression was utilized in our study to relegate the exact cause of death. We used standard datasets
like PHMRC and Matlab which were potentially accepted in medical field. The reason to utilize the
Multinomial logistic Regression is that most of the dataset is consisting of 0 and 1 values which
betoken the presence and absence of value in the attribute. We used three standard metrics like the
sensitivity, Chance Corrected Concordance (CCC) and Cause-specific mortality fraction (CSMF) for
a comparison of our model with precedent models like Insilico VA, Tariff and InterVA-4. Computed
results show that proposed model is better than the precedent models.
Paper Details
PaperID: 2635
Author's Name: Zainab Mohanad Issa Ansaf and Dr. Shaheda Akthar
Volume: Volume 11
Issues: Volume 11
Keywords: Verbal Autopsy, Cause of Death, Multinomial Logistic Regression, Chance Corrected Concordance (CCC), Cause-specific Mortality Fraction (CSMF).
Year: 2021
Month: November
Pages: 5857-5872