@article{Abdalrada_Ali Fahem Neamah_Hayder Murad_2024, title={Predicting Diabetes Disease Occurrence Using Logistic Regression: An Early Detection Approach}, volume={5}, url={https://journal.esj.edu.iq/index.php/IJCM/article/view/987}, DOI={10.52866/ijcsm.2024.05.01.011}, abstractNote={<p> <span class="fontstyle0">Diabetes disease is prevalent worldwide, and predicting its progression is crucial. Several model have been<br />proposed to predict such disease. Those models only determine the disease label, leaving the likelihood of developing the disease<br />unclear. Proposing a model for predicting the progression of disease becomes essential. Therefore, this article proposes a logistic<br />regression model to anticipate the likelihood of Diabetes syndrome incidence. The model exploit capabilities of logistic regression<br />by using sigmoid function. The model’s performance was evaluated using the Pima Indians Diabetes dataset and demonstrated<br />high accuracy, sensitivity, and specificity. The prediction accuracy rate was 77.6%, with a sensitivity of 72.4%, specificity of<br />79.6%, Type I Error of 27.6%, and Type II Error of 20.4%. Furthermore, the model indicates the feasibility of using laboratory<br />tests, such as Pregnancies, Glucose, Blood Pressure, BMI, and DiabetesPedigreeFunction, to predict disease progress. The<br />proposed model can aid patients and physicians in understanding the disease’s progression and implementing timely interventions</span> </p>}, number={1}, journal={Iraqi Journal For Computer Science and Mathematics}, author={Abdalrada, Ahmad and Ali Fahem Neamah and Hayder Murad}, year={2024}, month={Jan.}, pages={160–167} }