Deep Neural Network for Structured Data - A Case Study of Mortality Rate Prediction Caused by Air Quality

Dian Maharani, Hendri Murfi

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)

Abstract

The mortality rate is one of the important aspects in determining insurance premiums. The mortality rates have influenced by several factors, i.e., air quality. Therefore, we consider Deep Neural Network (DNN) model for prediction of the air quality-based mortality rate. In this paper, we examine two DNN architectures. The first architecture consists of five layers including an input layer, a hidden layer, two hidden dropout layers, and an output layer. The second architecture consists of four layers including an input layer, a hidden layer, a hidden dropout layer, and an output layer. We optimize dropout rates and activation functions to obtain the optimal accuracies. Our simulations show that the first DNN architecture produces a slightly better performance. The DNN architecture uses ReLu as activation function and applies a 40% dropout rate for both dropout hidden layers. This DNN architecture also gives slightly better accuracy than the standard one hidden layer Neural Networks.

Original languageEnglish
Article number012010
JournalJournal of Physics: Conference Series
Volume1192
Issue number1
DOIs
Publication statusPublished - 17 May 2019
Event2nd International Conference on Data and Information Science, ICoDIS 2018 - Bandung, Indonesia
Duration: 15 Nov 201816 Nov 2018

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