Analysis Effect of Gradient Descent Optimization on Logistic Regression in Brain Stroke Prediction
Keywords:
Gradient Descent Method, Logistic Regression, Statistical Analysis, Cost Function, MLAbstract
This work aims to investigate how gradient descent optimization affects the ability of Logistic Regression models to predict brain strokes. For binary classification problems like predicting strokes, logistic regression is commonly employed due to its clarity and simplicity. However, the optimization algorithm selected can have a significant impact on the convergence rate and overall forecast accuracy. In this work, the effectiveness of models developed using different optimization techniques is compared to that of models trained using Gradient Descent. We conducted a comprehensive study to evaluate the performance of two well-known approaches, the Gradient Descent Method (GDM) and Logistic Regression (LR), to learn and predict the occurrence of brain strokes. Through the use of these methodologies and their advancement, our research intends to create the most accurate predictive model, giving healthcare providers accurate and reliable stroke risk evaluations. The long-term goal of this research is to create a statistical model that not only explains the link between the dependent and independent factors but also provides informative data on the impact of certain patient features on the risk of brain stroke. By accomplishing this, we want to pave the way for more targeted and effective medicines, which will lead to better patient outcomes and a sharp drop in fatal brain stroke cases in the years to come.
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