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    Accuracy enhancement of brain epilepsy detection by using of machine learning algorithms
    (Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü, 2020) Al-Dahhan, Rand Natiq Neamah; Uçan, Osman Nuri
    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.