Makine öğrenmesi yöntemleri ile diyabet hastalığı tahminleme
Küçük Resim Yok
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
İstanbul Beykent Üniversitesi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Günümüzde pek çok alanda yaygın olarak kullanılan yapay zeka, insanların karmaşık sorunları daha kolay ve hızlı çözümlemelerine yardımcı oluyor. Yapay zeka, insan zekasını taklit ederek, topladığı veriler aracılığıyla öğrenip, kendini geliştirebiliyor ve yenilik yapabiliyor. Sağlıkta alanında da yaygın olarak kullanılan makine öğrenimi teknikleri, erken teşhis ve yerinde teşhis sağlayarak, kolaylıklar sunmaktadır. Bu çalışma kapsamında, Spyder aracı üzerinde Python programlama dili kullanılarak, Logistic Regression, XGBoost, Naive Bayes, En Yakın Komşu, Destek Vektör Makineleri, Karar Ağacı gibi yaygın makine öğrenmesi teknikleri Diyabet verisine uygulanmış ve şeker hastalığı tespiti yapılmıştır. Diyabet hastalığı tespiti için makine öğrenimi yöntemleri uygulanıp, karşılaştırılarak, erken teşhis ve gelecekte teşhis öngörüsü için en etkili yöntemler belirlenmiştir. Bu çalışmanın amacı, toplam 768 hastadan toplanan verilere yapay zeka algoritmalarını uygulayarak, bu tekniklerden elde edilen sonuçlar kıyaslanmış, diyabet teşhisini en yüksek doğruluk oranında sağlayan algoritmanın bulunmasını kapsamaktadır. Bu tezde kullanılan yapay zeka algoritmaları Lojistik Regresyon, KNN, XGBoost, Naive Bayes, Rastgele Orman, Destek Vektör Makineleri, Karar Ağacı'dır. Algoritmalardan elde edilen sonuçlar kıyaslandığında en yüksek doğruluk oranı %96 ile Rastgele Orman ve KNN algoritması ile elde edilmiştir.
Artificial intelligence, which is widely used in many fields today, helps people to solve complex problems more easily and quickly. Artificial intelligence can learn, improve and innovate through the data it collects by imitating human intelligence. Machine learning techniques, which are also widely used in the field of health, provide convenience by providing early diagnosis and on-site diagnosis. Within the scope of this study, common machine learning techniques such as Logistic Regression, XGBoost, Naive Bayes, Nearest Neighbor, Support Vector Machines, Decision Tree were applied to Diabetes data by using Python programming language on the Spyder tool and diabetes detection was made. By applying and comparing machine learning methods for the detection of diabetes, the most effective methods for early diagnosis and prediction of future diagnosis are determined. The aim of this study is to find the algorithm that provides the highest accuracy rate for diabetes diagnosis by applying artificial intelligence algorithms to the data collected from a total of 768 patients, the results obtained from these techniques are compared. Artificial intelligence algorithms used in this thesis are Logistic Regression, KNN, XGBoost, Naive Bayes, Random Forest, Support Vector Machines, Decision Tree. When the results obtained from the algorithms are compared, the highest accuracy rate was obtained by the Random Forest and KNN algorithm with 96%.
Artificial intelligence, which is widely used in many fields today, helps people to solve complex problems more easily and quickly. Artificial intelligence can learn, improve and innovate through the data it collects by imitating human intelligence. Machine learning techniques, which are also widely used in the field of health, provide convenience by providing early diagnosis and on-site diagnosis. Within the scope of this study, common machine learning techniques such as Logistic Regression, XGBoost, Naive Bayes, Nearest Neighbor, Support Vector Machines, Decision Tree were applied to Diabetes data by using Python programming language on the Spyder tool and diabetes detection was made. By applying and comparing machine learning methods for the detection of diabetes, the most effective methods for early diagnosis and prediction of future diagnosis are determined. The aim of this study is to find the algorithm that provides the highest accuracy rate for diabetes diagnosis by applying artificial intelligence algorithms to the data collected from a total of 768 patients, the results obtained from these techniques are compared. Artificial intelligence algorithms used in this thesis are Logistic Regression, KNN, XGBoost, Naive Bayes, Random Forest, Support Vector Machines, Decision Tree. When the results obtained from the algorithms are compared, the highest accuracy rate was obtained by the Random Forest and KNN algorithm with 96%.
Açıklama
Anahtar Kelimeler
Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Computer Engineering and Computer Science and Control
