Accuracy enhancement of brain epilepsy detection by using of machine learning algorithms
[ X ]
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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. Data collection technique is vital to the processes of
prediction and hence it need to be selected with care. In this study, data is collected from the
hospitals using the data entry techniques more likely when battery of test are made. The data
entry system is hospital may be used to record the tests values for each case. A specialist doctor
is always available to manually diagnose the case and made the proper prediction of the
disease. Eventually, data is collectively used from different cases along with their target
(diagnosis) to be analyzed using machine learning approach. Prediction of the disease can be
done using the machine learning tools. This may provide high accuracy and time efficient mean
of disease diagnosis. Big data that represents brain disease dataset is used in this study from
various cases are referred during training of the algorithms. A brain 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.
Açıklama
Anahtar Kelimeler
Machine Learning, LSTM, FFNN, Random Forest, KNN, Naïve Bays, Preprocessing
Kaynak
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Al-Dahhan, Rand Natiq Neamah. (2020). Accuracy enhancement of brain epilepsy detection by using of machine learning algorithms. (Yayınlanmamış yüksek lisans tezi). Altınbaş Üniversitesi, Lisansüstü Eğitim Enstitüsü, İstanbul.
