## 6 More Comparisons with Alternative Approaches
### Practical Application and Experiences by Physicians  
The development of this s scientific method with ANN-ALO to predict chaotic EEG time series also required its practical value to be shown. Due to this, it was critical to gather some evidence for the practical value of the system. In order to achieve this, the ANN-ALO system was coded with the Java programming language in order to form a prediction software system that could be used directly in computers to analyze chaotic EEG data flows in a real manner. The developed software was distributed to neurosurgery politiclinics of four local hospitals in Isparta, Turkey. The hospitals were, respectively, Isparta State Hospital (from which the EEG data used in the study were obtained), the Hospital of Suleyman Demirel University (SDU), Meddem Hospital, and the Davraz Life Hospital. Currently, the software system is actively used by a total of six physicians from the related politiclinics of the hospitals, and in order to obtain enough information about the practical value of the system, anonymous interviews were done to obtain remarkable ideas from the experts for the developed system and solution approach. Statements received from the physicians were generally positive ones. Some remarkable statements noted are as follows: 'This software has improved my efficiency by ensuring predictive analyze of EEG' (_Meddem Hospital_). 'I am currently using the system for investigating epilepsy in my patients' (_Isparta State Hospital_). 'I think such a prediction approach \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Application** & **SVM** & **HMM** & **BG** & **ARIMA** & **ARM** & **K-NN** \\ \hline A1 & 2 (+12) & 6 (+8) & 12 (+2) & 10 (+4) & 13 (+1) & 4 (+10) \\ A2 & 8 (+6) & 7 (+7) & 11 (+3) & 10 (+4) & 13 (+1) & 9 (+5) \\ A3 & 8 (+6) & 13 (+1) & 10 (+4) & 9 (+5) & 12 (+2) & 7 (+7) \\ A4 & 7 (+7) & 12 (+2) & 11 (+3) & 8 (+6) & 9 (+5) & 10 (+4) \\ A5 & 3 (+11) & 10 (+4) & 9 (+5) & 12 (+2) & 13 (+1) & 4 (+10) \\ A6 & 3 (+11) & 10 (+4) & 12 (+2) & 11 (+3) & 13 (+1) & 8 (+6) \\ A7 & 5 (+9) & 12 (+2) & 13 (+1) & 11 (+3) & 10 (+4) & 6 (+8) \\ A8 & 8 (+6) & 11 (+3) & 10 (+4) & 6 (+8) & 12 (+2) & 7 (+7) \\ A9 & 5 (+9) & 7 (+7) & 11 (+3) & 13 (+1) & 10 (+4) & 6 (+8) \\ A10 & 3 (+11) & 13 (+1) & 12 (+2) & 7 (+7) & 11 (+3) & 9 (+5) \\ \hline
**Total Points** & 88 & 39 & 29 & 43 & 24 & 70 \\ \hline \end{tabular}
\end{table} 
Table 15: The ranks and obtained points from each approach for the prediction of the EEG time series (set 2). Figure 9: Ranking (with points) of the employed approaches used to predict the EEG time series in this study. can be useful for also cardiologists and other physicians dealing with medical time series' (_Davraz Life Hospital_). T am able to predict brain diseases better thanks to this system. It is something like I have an experienced assistant who does not know being tired' (_Hospital of SDU_). T will shape my future academic research in nature of EEG data after seeing that it is possible to perform effective analyzes with Machine Learning' (_Hospital of SDU_). 'The system is good enough at predicting future states of brain activity and making effective decision making in terms of treatments' (_Meddem Hospital_). 'The system can be used as a mobile application I think. Currently I need to use my laptop everywhere' (_Davraz Life Hospital_). 'I started to reduce my diagnosis time thanks to using that automated system for prediction and analyze of EEG data' (_Meddem Hospital_). 'Prediction done with this software is useful to see many details you can miss with human eyes' (_Isparta State Hospital_). 'This software is fast at analyzing future states of the electrical activity in human brain. I wonder its application on also animals' (_Davraz Life Hospital_). In addition to the interview, the physicians were also asked to fill out a small survey to try and evaluate the used software system in terms of usability, accuracy, speed, effectiveness, and novelty, respectively.