Application of Random Forest and Decision Tree Classifier Approach for The Survival of Heart Patients using Characteristic Extraction
DOI:
https://doi.org/10.63503/j.ijaimd.2024.5Keywords:
Cardiovascular Diseases (CVD), LR, SVM Machine Learning (ML)Abstract
In modern times cardio pulmonary diseases are increasing at an alarming rate. It is a range of serious disorders that affects both the heart (cardio) and the lungs (pulmonary) at times resulting in death. In spite of continuous develop-ment in the field of medical science – both in the surgical and oral drugs, death due to cardiac arrest has remained a major cause of headache for the global population. Research suggests that mortality due to cardiac failure is mainly occurring in the early morning due to high blood pressure fluctuations. An advanced technology like machine learning has shown a new horizon of hope in the medical arena. ML carefully helps to select the patterns best suitable for data sets with various types of algorithms along with different correlation techniques. There are many features that are responsible for the heart disease like age, BP, sodium, cretonne, ejection fraction etc. After identifying important features by applying feature selection and ranking techniques the selected features are used as an input for the different ML algorithms. After imple-menting Random Forest algorithm it was observed that two sets of data tree emerged as comparison of results. Firstly a Decision Tree Classifier was select-ed to control the entire data sets, as well as to check the results with the gen-erated tree for the selected samples. After generating selected samples its re-sults got compared with the classification graph of the decision tree to achieve better result. It was observed that ML algorithms like Random Forest and De-cision Tree Classifier Techniques can helps to predict the major reasons for cardio vascular diseases
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