Beyin Epilepsi Tespitini Kullanarak Doğruluk Geliştirme Makina Öğrenme Algoritmaları

Öz

Bilim ve mühendislik uygulamalarında veriler hayati bir rol oynamıştır; doğru veri analizi, bu uygulamaların ekonomik değerini artırır. Makine öğrenimi araçları büyük verileri sınıflandırmak için kullanılır ve veriler içindeki gizli kalıpların bulunmasını sağlar. Bu gelecek tahmini ile ilgili önemli avantajları sağlayabilir. Sonuçta elde edilen bilgiler pratik sistemleri sadece karlı olan şeyleri geliştirmek için de kullanılabilir. Başka bir şekilde bakıldığında, şirkete veya kuruluşa zarar verebilecek hoş olmayan olayların önlenmesine de yardımcı olur. Beyin epilepsi hastalığı tahmin sistemi dört farklı algoritma kullanılarak uygulanır: Naive Bayes algoritması, K-en yakın komşular algoritması, rastgele orman algoritması ve uzun kısa süreli bellek sinir ağı. Performans ölçümleri de dört aracın tahmin performansındaki farkı değerlendirmek için başlatılır. Tahmin doğruluğu, bu dört yöntem için sırasıyla 33,035, 95, 61,195 ve 96,79 olarak kaydedildi.

Anahtar Kelimeler

• LSTM
• FFNN
• Rastgele Orman
• KNN
• Naive Bayes

Proje Numarası

1

Accuracy Enhancement of Brain Epilepsy Detection by Using of Machine Learning Algorithms

Öz

Data has gained vital role in science and engineering applications; the proper data analysis has made it possible to boost the economical worthiness of those applications. Machine learning tools are used to classify the big data in order to discover the hidden patterns in them. That may lead to noteworthy advantages that related to future prediction of the data. The resultant information can be used to enhance the practical systems in such way only the profitable thing can be come on then. In other way, it helps to prevent any unpleasant occurrence that may harm the company or the organization. A brain epilepsy disease prediction system is implemented using four different algorithms namely: Naïve Bays algorithm, K-Nearest Neighbours algorithm, Random Forest algorithm and Long Short Term Memory Neural Network. The performance metrics are also initiate in order to evaluate the difference in prediction performance of the four tools. The accuracy of prediction the disease was recorded more likely 33.035, 95, 61.195 and 96.79 for the Naïve Bays, Random Forest, K-Nearest Neighbour and Long Short Term Neural Network.

Anahtar Kelimeler

• LSTM
• FFNN
• Random Forest
• KNN
• Naïve Bays

Destekleyen Kurum

altinbas university

Proje Numarası

1

Teşekkür

thank's for my supervisor

Kaynakça

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