## 4 Discussion
### Applicability of ML to Clinical Practice
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **Advantages** & **Disadvantages** \\ \hline K-means Clustering & Simple clustering approach. & Requires a number of clusters in advance. \\  & Efficient clustering method. & Handling categorical attributes cause problems. \\  & Method is easy to be implemented. & Results vary considerably in the presence of outliers. \\ \hline DBSCAN / SOMS & Simple clustering approach. & Handling categorical attributes cause problems. \\  & A number of clusters in advance is not required. & Results vary considerably in the presence of outliers. \\  & Efficient clustering method. & \\ \hline SVM & Better accuracy compared to other classifiers. & High computational cost. \\  & Overfitting problem is not so great as in other methods. & Training process requires more time than other methods. \\ \hline ID3 & There are no domain requirements. & Results are restricted to one output attribute. \\  & Exact value results are provided for various actions, & Only categorical output is generated. \\  & minimising the ambiguity of complex decisions. & Classifier performance depends on \\  & High dimensional databases are processed easily. & the type of dataset, making it unstable. \\  & Classifier and output are easy to be interpreted. & \\ \hline KNN & Method is easy to be implemented. & Large storage space is required. \\  & Training process requires low computational cost. & Sensitivity to databases with high noise. \\  & Testing process requires high computational cost. & Testing process requires high computational cost. \\ \hline Naive Bayes & Method is easy to be implemented. & Low accuracy is provided in cases where \\ Bayesian Networks & Method is speeder and provide more accuracy in high & exists dependence between variables. \\  & dimensional databases than other methods. & \\ \hline Linear regression & Better accuracy compared to other classifiers. & Results vary considerably in the presence of outliers. \\  & Complex relationships between dependent and & Training process requires more time than other methods. \\  & independent variables are identified easily. & Classifier performance depends on the type of dataset, \\  & & making it unstable. \\  & Only numerical output is generated. & \\ \hline Logistic regression & Better accuracy compared to other classifiers. & Results vary considerably in the presence of outliers. \\  & Complex relationships between dependent and & Training process requires more time than other methods. \\  & independent variables are identified easily. & Classifier performance depends on the type of dataset, \\  & & making it unstable. \\ \hline Neural network & Complex relationships between dependent and & High possibility of local minima. \\  & independent variables are identified easily. & High possibility of overfitting problem. \\  & Ability to handle databases with high noise. & Classifier is difficult to be interpreted. \\  & A previous feature extraction task is not required. & High computational time is required if there is \\  & a large number of layers. & No explanation or justification of decisions can be given, \\  & & i.e., a “black box” characteristic. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Advantages and disadvantages of described algorithms. seen, the prediction will fail, with negative consequences if we address the detection of cancer or a neurodegenerative disease. ML algorithms, through their representation, evaluation, and optimization components [194], benefit from the availability of large amounts of data and powerful hardware architectures to represent more complex statistical phenomena than traditional approaches, while DL allows for identifying previously hidden patterns, extrapolate trends, and predict results in a wide spectrum of problems, trying to \"learn\" an approach to some function. ML techniques are currently applied to medical records in clinical practice to predict, for example, which patients are at greatest risk of readmission to hospital or who are unlikely to follow prescribed treatments.