Optimization of Random Forest Algorithm Using Random Search for Alzheimer's Disease Detection

Abstract

Alzheimer's disease is a type of neurodegenerative disorder that causes a decline in cognitive function. Early detection is crucial to enable more effective interventions and slow the progression of the disease. However, the diagnosis of Alzheimer's disease often faces challenges, particularly in detecting the early stages of the disease from complex and diverse medical data. This study aims to optimize the Random Forest algorithm using the Random Search method for detecting Alzheimer's disease. The Random Forest algorithm was applied as the primary model in this research, while hyperparameter optimization was performed using the Random Search method to improve model performance. The results showed that the Random Forest model without optimization achieved an accuracy of 96%. After performing hyperparameter optimization, the model's accuracy increased to 97%. In conclusion, the application of hyperparameter optimization using the Random Search method successfully enhanced the performance of the Random Forest model. The resulting model provides more accurate predictions, making it a reliable tool for the early detection of Alzheimer's disease.