Unveiling Alzheimer’s Early: A Comparative Exploration of Machine Learning Methods for Disease Detection

Artificial Intelligence has shown its revolutionary capabilities in the field of healthcare, by transforming the approach in handling complex medical tasks. These technological advancements have greatly influenced the early identification of Alzheimer’s disease, a neurodegenerative condition that is extremely critical. Timely identification is crucial in improving patient outcomes, and AI helps analyze massive datasets and thereby help predict and identify these diseases faster. These technologies can develop prediction models to identify high-risk individuals even before obvious symptoms show. They detect subtle patterns in clinical records, neuroimaging scans, and other biomarker measures. According to the World Health Organization, the number of individuals with dementia is anticipated to double every 20 years, reaching 78 million in 2030 and 139 million by 2050. This anticipated rise in the occurrence of Alzheimer’s highlights the significance of prompt identification and assistance. Alzheimer’s disease erodes cognitive abilities of the patient as it progresses, and early signs provide a substantial obstacle. The ability of AI and ML to evaluate neuroimaging data, like MRI scans plays a key role in this situation. These technologies are capable of detecting small structural changes in the brain that occur prior to obvious symptoms. Furthermore, AI powered predictive models can create tailored risk profiles by combining data from cognitive tests, genetic patterns, and other data from wearable devices. Early detection significantly improves patient outcomes and lessens caregiver stress by enabling patients to get timely medical attention, treatments, and support services. Machine learning models applied in this study, which include SVM, Decision Tree, Random Forest, and XGBoost, are rigorously evaluated using metrics like accuracy, precision, recall, and the F1 score. All of the chosen models showed better performance, while the XGBoost model, optimized with its best parameter, showcased a notable test accuracy of 84%, highlighting its effectiveness in classification of Alzheimer’s disease.