Early identification of Alzheimer's disease: Leveraging support vector machines for predictive insights

The early identification of Alzheimer&s;s disease is crucial for its efficient management. This research focused on the application of using ‘supervised learning’ techniques such as the Support Vectors Machines (SVM) to improve the prediction accuracy of the Alzheimer&s;s disease. Using patient&s;s cognitive data, along with their medical history and results from cognitive tests, the algorithm identifies potential illness signs. The model is designed to handle large medical data and categorize people based on their probability of having Alzheimer&s;s health risks. The approach enables more effective and timely medical interventions. In addition to enhancing diagnostic precision, this method helps to analyze the nature of the disease&s;s progression in greater detail, making it valuable for delivering interventions at earlier stages.