Mortality rate of human detection using decision tree

The study aims to develop a decision tree-based model for detecting the morality rate of humans, addressing the ethical considerations associated with mortality prediction. The model utilizes a decision tree algorithm to analyze a comprehensive set of factors and features derived from a diverse dataset comprising individual attributes, medical histories, lifestyle factors, and socio-demographic information. The decision tree algorithm is employed due to its ability to handle both numerical and categorical data, making it suitable for capturing complex relationships between variables. The dataset used for model training and evaluation is sourced from a large-scale population-based study, encompassing a wide range of subjects across different age groups, geographical locations, and socio-economic backgrounds. The developed decision tree model aims to provide accurate predictions regarding the mortality rate of individuals, thereby enabling timely intervention and targeted healthcare interventions. Ethical considerations related to privacy, data protection, and consent are carefully addressed throughout the study, ensuring compliance with established regulations and guidelines. The performance of the decision tree model is assessed using various evaluation metrics, including accuracy, precision, recall, and F1 score, using cross-validation techniques. The results obtained from the evaluation demonstrate the effectiveness of the decision tree-based approach in detecting mortality rates, offering valuable insights for healthcare professionals, policymakers, and researchers.

KEYWORDS: morality dataset, decision trees,