## 3 Results
### Patient Profiling--Clustering
The indicator value measures the similarity of an object to its own cluster compared to other clusters. It can be used to study the separation distances between resulting clusters. The ratio is calculated by averaging the distance between clusters (a) and the distance to the nearest cluster (b) for each sample. Additionally, the PrefixSpan sequence pattern discovery method was employed [9]. This method aims to identify the most frequently occurring sequences of events based on a list of sequences and a minimum support value that determines their frequency. The algorithm also identifies the longest sequences, assuming a minimum number of repetitions. The algorithm also identifies the longest sequences, assuming a minimum number of repetitions. The results are sorted based on the specified criteria. The method presented produces a set of sequence patterns. Each tuple in the set contains two values: the support of the pattern and the recognized sequence, which is a list of cyclically repeated values. The data were discretized (qualitized) using the KBinsDiscretizer method [10] from the sklearn.preprocessing library to ensure they could be used with the PrefixSpan method. Discretization is the process of transforming continuous data into discrete values for numerical processing. The method used grouped variable values into countable containers and assigned each a unique integer while maintaining the ordinal relationship between them. This research study proposes a third algorithm for classifying sequences into individual categories using LSTM recurrent neural networks [11]. The recurrent network is trained using the sequence and the SOM clustering result from the previous method. Test data are fed to the trained network to determine the correct operation of the algorithm. The resulting classifier can be used to classify patient data. A recurrent neural network is a type of artificial neural network that creates a directed or undirected graph along a time sequence [12]. Its use demonstrates the dynamic behavior of data over time. Unlike feedforward neural networks, recurrent neural networks use their internal state (memory) to analyze sequences of variable-length input data. LSTM (Long Short-Term Memory) is a type of recurrent neural network that is designed to work with sequential data such as text, time series, audio, or action sequences. It has the ability to remember long-term dependencies in data [13]. LSTM is distinguished by its capacity to retain information for longer periods than standard RNNs, thereby eliminating the vanishing gradient problem that occurs in classic RNNs. This is accomplished through the use of special structures known as 'gates' that determine which information should be retained and which should be discarded during sequence processing [14]. The data are divided into training and testing sets (80:20) to train the model and evaluate its performance, as well as its accuracy on data it has not seen before. The utilized recurrent neural network model consists of the following layers:  
* Input layer: The number of features in the input data depends on the lengths of series (sequences). Hidden layers:
* LSTM layer: 100 neurons--A type of recurrent network that utilizes special units in addition to standard units containing memory. Thanks to these special cells, they can store information in memory for a long time, allowing them to learn long-term dependencies. * Dropout: 50% dropout rate. No neurons; it is a regularization layer. It is a layer that randomly sets input units to 0 with a certain frequency at each step during training, which helps prevent overfitting. * Dense layer (hidden layer): 100 neurons with ReLU activation function. * Output layer: Dense layer: The number of outputs, which is equal to the number of clusters (25 in this case) with softmax activation function.