Improving Disease Diagnosis with Integrated Machine Learning Techniques

Özge H. Namlı*, Seda Yanık

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde BölümKonferans katkısıbilirkişi

1 Atıf (Scopus)

Özet

As the digital transformation is constantly affecting every aspect of our lives, it is important to enhance and use machine learning models more effectively also in the healthcare domain. In this study, we focus on the application of machine learning algorithms for disease diagnosis in order to support decision making of physicians. Different classification methods are used to predict the diameter narrowing in the heart using an anonymous dataset. In order to increase the prediction ability of the machine learning algorithms, we employ different feature extraction methods such as Autoencoder, Stacked Autoencoder, Convolutional Neural Network, and Principal Component Analysis methods and integrate each feature extraction method with the classification methods. Then, we compare the prediction performances of individual and feature-extraction-integrated classification methods. It is shown that the prediction performance of the classification methods increase when integrated with feature extraction methods. However, it is concluded that not all feature extraction methods work as well with all classification methods. When a specific classification method is integrated with the appropriate feature extraction method, a better improvement in the prediction performance can be obtained.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıIntelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference, Volume 2
EditörlerCengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, A. Cagri Tolga, Selcuk Cebi
YayınlayanSpringer Science and Business Media Deutschland GmbH
Sayfalar53-61
Sayfa sayısı9
ISBN (Basılı)9783031091759
DOI'lar
Yayın durumuYayınlandı - 2022
EtkinlikInternational Conference on Intelligent and Fuzzy Systems, INFUS 2022 - Izmir, Turkey
Süre: 19 Tem 202221 Tem 2022

Yayın serisi

AdıLecture Notes in Networks and Systems
Hacim505 LNNS
ISSN (Basılı)2367-3370
ISSN (Elektronik)2367-3389

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???event.eventtypes.event.conference???International Conference on Intelligent and Fuzzy Systems, INFUS 2022
Ülke/BölgeTurkey
ŞehirIzmir
Periyot19/07/2221/07/22

Bibliyografik not

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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