MACHINE LEARNING-BASED MODEL DEVELOPMENT IN MOBILE HEALTH APPLICATIONS AND DEVICE-CLOUD INTEGRATION

Özge Çiçek; Sema Candemir

doi:10.17780/ksujes.1624304

TR EN

MOBİL SAĞLIK UYGULAMALARINDA MAKİNE ÖĞRENMESİ TEMELLİ MODEL GELİŞTİRME VE MODELİN CİHAZ-BULUT ENTEGRASYONU

Öz

Mobil Sağlık (mHealth), mobil cihazlar ve kablosuz teknolojiyi kullanarak sağlık hizmetlerini destekleyen uygulamaları içerir ve sağlık hizmetlerine yaygın erişim sağlar. Makine öğrenimi (ML) alanındaki son gelişmeler, hastalık teşhisi ve takibini iyileştirerek sağlık hizmetlerini geliştirmiştir. Ancak, özellikle görüntü işleme ve derin öğrenmeye dayalı makine öğrenimi modellerini mobil cihazlara entegre etmek ve çalıştırmak, sınırlı işlem gücü ve depolama kapasitesi nedeniyle zor olabilir. Bu çalışma, bir ML tabanlı modelin geliştirilmesi ve mobil cihazlar ile bulut ortamlarına entegrasyonu adımlarını açıklamaktadır. Örnek mHealth uygulaması olarak MobileNet mimarisi kullanılarak bir cilt hastalığı tahmin modeli geliştirilmiştir. Model performansını artırmak için transfer öğrenimi, veri artırma ve focal loss gibi teknikler kullanılmıştır. Eğitilmiş mHealth modeli daha sonra bir mobil cihaza ve bulut ortamına entegre edilmiştir. Cihaz üzerindeki model, bulut tabanlı modele kıyasla daha hızlı tahmin süreleri (cihaz üzerindeki model ortalama 108,3 ms) sergilemiştir (bulut tabanlı model ortalama 1281,2 ms). Cihaz üzerindeki dağıtım, veri gizliliği ve çevrimdışı işlevsellik sağlarken, bulut yaklaşımı ölçeklenebilirlik ve daha kolay güncellemeler sunmuş, ancak gecikme süresi ve veri güvenliği açısından dezavantajlar yaratmıştır. Bu çalışma ML modellerinin mHealth uygulamalarına entegrasyonunun uygulanabilirliğini göstermekte, karşılaştırmalı bir analiz sunmakta ve performans, maliyet ve kullanılabilirlik arasında bir denge kurmanın önemini vurgulamaktadır.

Anahtar Kelimeler

MACHINE LEARNING-BASED MODEL DEVELOPMENT IN MOBILE HEALTH APPLICATIONS AND DEVICE-CLOUD INTEGRATION

Abstract

Mobile Health (mHealth) uses mobile devices and wireless technology to support healthcare practices, enabling widespread access to health services. Recent advancements in machine learning (ML) have enhanced healthcare by improving disease diagnosis and monitoring. However, integrating and executing machine learning models, especially those based on image processing and deep learning, on mobile devices can be challenging due to limited processing power and storage capacity. This study describes the steps of developing an ML-based model and its integration into mobile devices and cloud environments. A skin disease predictor using the MobileNet architecture was developed as a use-case mHealth application. Techniques such as transfer learning, data augmentation, and focal loss were employed to enhance model performance. The mHealth model was then integrated into a mobile device and the cloud environment. The on-device model exhibited faster prediction times (average 108.3 ms) compared to the cloud-based model (average 1281.2 ms). While on-device deployment ensured data privacy and offline functionality, the cloud approach provided scalability and easier updates, but at the expense of latency and data security. By providing a comparative analysis, this work demonstrates the feasibility of integrating ML models into mHealth applications, emphasizing the importance of balancing performance, cost, and usability.

Keywords

Supporting Institution

This research is partially supported by the TUBITAK-BIDEB 2232 International Fellowship program under the grant number 121C085.

Project Number

121C085

Ethical Statement

The study has not been submitted to another journal for consideration.

Thanks

This research is largely based on the work conducted by Özge Çiçek in her master’s thesis titled "Mobil Sağlık Uygulamalarında Makine Öğrenmesi Temelli Model Geliştirme ve Modelin Cihaz-Bulut Dağıtımı" (Cicek, 2024)

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Details

Primary Language

English

Subjects

Machine Learning (Other)

Journal Section

Research Article

Authors

Özge Çiçek
0009-0004-8982-3341
Türkiye

Sema Candemir ^*
0000-0001-8619-5619
Türkiye

Publication Date

June 3, 2025

Submission Date

January 21, 2025

Acceptance Date

March 18, 2025

Published in Issue

Year 2025 Volume: 28 Number: 2

DOI

https://doi.org/10.17780/ksujes.1624304

IZ

https://izlik.org/JA28XW54WN

Cite

RIS / Bibtex

APA

Çiçek, Ö., & Candemir, S. (2025). MACHINE LEARNING-BASED MODEL DEVELOPMENT IN MOBILE HEALTH APPLICATIONS AND DEVICE-CLOUD INTEGRATION. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(2), 872-882. https://doi.org/10.17780/ksujes.1624304