Verı madencılığı teknikleri kullanarak kalp hastahğı tanısı

Lately, large masses of data have been generated due to the ongoing approaches in biotechnology and fitness sciences areas. It combines clinical information and genetic data which included in Electronic Health Records (EHRs). On the other side, it is required to recognize symptoms, which can wrongly convince the human health in addition to placing economic burdens on their shoulders, in an early stage to avoid many difficulties. Lately, several data mining procedures have played a vital role in developing automated operations that can identify syndromes efficiently and correctly. In this thesis, we satisfy some of the research disciplines that have employed either the data mining procedures for identifying symptoms. Additionally, a set of well-known data mining methods including Decision Trees (j48), Naïve Bayes, Multilayer Perceptron (MLP), and Random Forest (RF) has been assessed in performing the classification task using a publicly available heart diseases dataset.