The application of artificial intelligence (AI) in all fields and in medicine in particular is gaining more and more popularity. Machine learning (ML) is part of AI, in which computers use statistical methods for self-learning without being explicitly programmed to do so. The challenge Titanic: Machine Learning from Disaster is probably the most popular among researchers entering the field of artificial intelligence and machine learning.
In almost all cases, ML goes through several successive stages: definition of the problem, data gathering, data preparation, exploratory analysis, feature engineering; model creation, and model evaluation. The following manuscript briefly describes each of them. With their help, a model for the challenge "Titanic: Machine Learning from Disaster" was created. This model outperformed most of the models featured in the competition. It achieved a 99.8% better result than the other models presented.
Machine learning could improve the accuracy of different models with the same data (equal start). In this way, proper data processing could lead to higher accuracy, which in medicine translates into improved clinically significant endpoints.
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