]]> Another key-point to the development of this app relates to the importance of marketing and social events on the effect of tourism. Many big cities today live off of the money created from tourism. Whether we like it or not, foreign people like to spend time in our cities and are more inclined to spend their money on as many events and traditions as they possibly can in the short amount of time they have. Sarajevo is no exception, as it has long stood as the “Jerusalem of Europe”, bringing in tourists from all over the world. Hence, It would be important to inform the tourists of the main attractions and places to spend their valuable time at.


Those two key-points make up the bulk of the idea behind this application: Create an online information system for tourists and locals to discover new events and places to spend their time at. Not only would it serve as a convenient tool to have at everyday disposal, but it would also allow for marketing and discovering lesser known events and places all in one place, along with many other useful features, ensuring everyone stays up to date with the current situation in Sarajevo.
]]> Disorder - ASD diagnosis. ASD is detected based on behavioral screening which is time consuming and
can only be taken by a medical professional. The idea is to find a smaller number of features that are still
able to equally well provide satisfying results and not lose the accuracy, sensitivity nor specificity. Some
of the algorithms mostly used in recent studies were Artificial Neural Network - ANN and Alternating
Decision Trees - ADTrees. The researches usually use WEKA software package for applying the algorithm
and obtaining results.]]> being diagnosed with brain cancer, group 0 has the highest percentage in one month after the diagnosis, while group -2 has the lowest percentage. Most genes shift their clusters when Kmeans and hierarchical clustering techniques we compared with the genes from the control and disease groups. The result of the measure of dissimilarity between the genes expression patterns indicates that the K-means technique outperforms the hierarchical technique with a higher rate
of change in the cluster.]]> Heart strokes remain one of the leading health risks in the world today. Timely prediction can significantly improve patient outcomes and healthcare resource allocation. This study aims to harness machine learning techniques to develop efficient predictive models for the early detection of heart strokes.
Research is based on a dataset created by combining different (five) datasets. The dataset encompasses patient demographics, clinical measurements, and historical medical records. The analysis focused on five machine learning models: Logistic Regression, Decision Tree, K-Nearest Neighbors, Random Forest, and Support Vector Machine.
The goal was not only to test different algorithms, but also to understand how data preparation, feature selection, and model choice impact the final results. The models were trained and tested on both the original dataset and an extended version, where new features were added by combining existing ones.
The results showed that models such as Logistic Regression, Decision Tree, and KNN performed better when applied to the original data. The Decision Tree model achieved an accuracy of 87.8% and an F1 score of 0.881, while Logistic Regression and K-Nearest Neighbors each attained F1 scores of 0.850 and 0.849, respectively. On the other hand, Random Forest and SVM showed significant improvements with the extended dataset. Random Forest performed the best overall, with an F1 score of 0.920 and an accuracy of 91.6% with enhanced results.
SVM also benefited from enhanced results, improving its F1 score from 0.892 to 0.879, which highlights how specific models can leverage additional features for improved generalization.
This tool could help detect risks earlier, allowing for timely interventions and prevention, thereby reducing the burden of strokes on healthcare systems and improving patient care. Limitations include data quality and availability, as well as potential bias in healthcare records.]]>