Paper Key : IRJ************853
Author: Mohammed Guhdar Mohammed
Date Published: 14 Jan 2023
Abstract
Logistic regression is a type of statistical model that can be used to predict the probability of an outcome occurring, given a set of input features. In the case of diabetes, logistic regression could be used to predict the probability that an individual has diabetes, based on a set of risk factors such as age, family history, and body mass index. Logistic regression is a popular method for predicting binary outcomes, like the presence or absence of a disease, and can be a useful tool for identifying individuals at high risk of developing diabetes. However, Logistic Regression is a simple method and its predictions are based on a linear combination of input features, it may not work very well when the relationship between the inputs and the outcome is non-linear or when there is a high degree of interaction between the input features, In this research, we are going to apply logistic regression on a Kaggle dataset to predict the probability of an individual having diabetes, based on a set of risk factors such as age, family history, and body mass index. The model will be trained on the data available in the Kaggle dataset and will be used to make predictions on new, unseen data.
DOI LINK : 10.56726/IRJMETS32941 https://www.doi.org/10.56726/IRJMETS32941