This chapter is written in order to give students general summary of basic statistical methods, techniques, indicators and procedures. However emphasis is on the application of those that are frequently used in biomedical research. Statistics is science about data, but data that represent numbers with their context. Therefore, in this chapter the topics of population, samples, types of variables, descriptive statistics and statistical inference will be briefly covered. At the end, correlation and regression analysis will be briefly presented. The aim of this chapter is to give a very short overview of the main principles; techniques and procedures that should be used in order to obtain and understand analyzed biomedical data.

Traditional statistical models as tools for summarizing patterns and regularities in observed data can be used for making predictions. However, statistical prediction models contain small number of important predictors, which means limited informative capability. Also, predictive statistical models that provide some type of regular patterns that seems to hold true statistically, are used without previous understanding of causal mechanisms in the observed data. Machine learning methods like artificial neural networks as a special class of artificial intelligence provide the ability to interpret and understand data in more sophisticated way. NN methods use non-linear algorithms, considering links and associations between parameters, while statistical use linear processes to improve only short-term prediction's accuracy by minimizing cost function, often one-step-ahead, without considering medium and long-term ones. Disregarding that designing an optimal artificial neural network is very complex process, they are considered as potential solution for overcoming main flaws of statistical prediction models. However, they will not automatically improve predictions accuracy, so several artificial neural networks and traditional statistical methods are evaluated and analyzed through accuracy measures for prediction purposes in various fields of applications. Based on gained results, couple of techniques for improving artificial neural networks are proposed to get better accuracy results than statistical predictive methods.

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Compared to traditional statistical models, Machine Learning (ML) algorithms provide the ability to interpret, understand and summarize patterns and regularities in observed data for making predictions in an advanced and more sophisticated way. The main reasons for the advantage of ML methods in making predictions are a small number of significant predictors of the statistical models, which means limited informative capability, and pseudo-correct regular statistical patterns, used without previous understanding of the used data causality. Also, some ML methods, like Artificial Neural Networks, use non-linear algorithms, considering links and associations between parameters. On the other hand, statistical models use one-step-ahead linear processes to improve only short-term prediction accuracy by minimizing a cost function. Although designing an optimal ML model can be a very complex process, it can be used as a potential solution for making improved prediction models compared to statistical ones. However, ML models will not automatically improve prediction accuracy, so it is necessary to evaluate and analyze several statistical and ML methods, including some artificial neural networks, through accuracy measures for prediction purposes in various fields of applications. A couple of techniques for improving suggested ML methods and artificial neural networks are proposed to get better accuracy results

This study aimed to investigate the application of machine learning techniques for disease prediction. Three popular machine learning algorithms, Random Forest, Support Vector Machines and Naive Bayes, were employed and their performance was evaluated. Results showed that the best performing model was based on Random Forest algorithm with the average accuracy of 87%. This model has been additionally tuned in order to achieve even better performance, which resulted with 90% accuracy. This study highlights the potential of AI in disease prediction and provides insights into the importance of algorithm selection and tuning for optimal performance.