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Journals / Politeknik Dergisi / 2019 / Cilt: 22 - Sayı: 3

Diabetes Diagnosis with Intelligent Optimization Based Support Vector Machines

Zeki Optimizasyon Tabanlı Destek Vektör Makineleri ile Diyabet Teşhisi

UTKU KÖSE
Pages
557–566
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Hereby, the study also confirms that this followed approach has a significant potential for Artificial Intelligence based diagnosis.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Artificial Intelligence is one of the most important scientific fields that can be applied effectively in different real world problems and has been shaping our future for a long time.
📊
Key Findings & Data:Obtained findings showed that hybrid systems formed with different algorithms show different-level success but in general, good level accurate results can be achieved via intelligent optimization-SVM approach.
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Conclusion & Impact:Hereby, the study also confirms that this followed approach has a significant potential for Artificial Intelligence based diagnosis.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Zeki optimizasyon · destek vektör makineleri · diyabet teşhisi

Abstract

Artificial Intelligence is one of the most important scientific fields that can be applied effectively in different real world problems and has been shaping our future for a long time. While there are various types of problems in which it is applied, medical diagnosis is one of the most remarkable one among them. Moving from the explanations, objective of this study is to realize diabetes diagnosis by using intelligent optimization based Support Vector Machines (SVM). In this context, five ones of today’s recent intelligent optimization algorithms were used for optimizing a non-linear SVM, which is using a Gaussian-RBF kernel function. Obtained findings showed that hybrid systems formed with different algorithms show different-level success but in general, good level accurate results can be achieved via intelligent optimization-SVM approach. Hereby, the study also confirms that this followed approach has a significant potential for Artificial Intelligence based diagnosis.

Özet

Yapay Zekâ, farklı gerçek dünya problemlerine etkin bir şekilde uygulanabilen ve geleceğimizi uzun bir süredir şekillendiren, önemli bilim alanlarından birisidir. Uygulandığı problem türleri çok çeşitli olmakla birlikte, bunlardan en dikkat çekenlerinden birisi de medikal teşhistir. Açıklamalardan hareketle bu çalışmanın amacı, zeki optimizasyon tabanlı Destek Vektör Makineleri’ni (DVM) kullanarak diyabet teşhisi gerçekleştirmektir. Bu bağlamda, günümüz güncel zeki optimizasyon algoritmalarından beş tanesi Gaussian-RBF kernel fonksiyonu kullanan bir non-lineer DVM’yi optimize etmek amacıyla kullanılmıştır. Elde edilen bulgular, farklı algoritmalar ile kurulmuş hibrit sistemlerin, farklı düzeyde başarımlar gösterdiğini ancak genel anlamda zeki optimizasyon-DVM yaklaşımıyla diyabet teşhisinde yüksek oranda tutarlı sonuçlar elde edilebildiğini ortaya koymuştur. Çalışma bu yönüyle izlenen yaklaşımın Yapay Zekâ tabanlı teşhis açısından önemli bir potansiyele sahip olduğunu da teyit etmektedir.

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Cite This Article

KÖSE, U. (2019). Diabetes Diagnosis with Intelligent Optimization Based Support Vector Machines. Politeknik Dergisi, Cilt: 22 - Sayı: 3, 557–566. https://ajindex.com/articles/307372

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