Statistical analysis and prediction of data for Alzheimer's disease and mild cognitive impairment based on multiple machine learning algorithms

This paper focuses on the characteristics and diagnostic methods of two neurodegenerative diseases, Alzheimer's disease and mild cognitive impairment, as well as the application of machine learning in the medical field. Firstly, through the statistical analysis of patients' age and gender, it is found that patients with Alzheimer's disease tend to be older than patients with mild cognitive impairment, while the number of female patients are both larger than that of male. These results provide an important basis for subsequent classification prediction. Next, this paper uses a variety of machine learning algorithm models for prediction, including Decision Tree, Random Forest, Logistic Regression, Naive Bayes, KNN, and SVM. These models are all based on different algorithmic principles and data processing methods to achieve classification prediction of patients with Alzheimer's disease and mild cognitive impairment. By comparing the prediction accuracy of each model, it is found that Decision Tree and Naive Bayes models have the best prediction effect, with a prediction accuracy of 75%; while Random Forest and Logistic Regression models also show high prediction accuracy. In contrast, the KNN and SVM models had a poor prediction effect of 69.2%. These results suggest that the use of decision tree or Naive Bayes models for prediction should be prioritised when classifying patients with Alzheimer's disease and mild cognitive impairment. Finally, this paper highlights the importance of machine learning in the medical field. By analysing and processing large amounts of medical data, machine learning can help doctors diagnose diseases more accurately and provide more effective treatment options. The application of these techniques will help improve the quality of life of patients and advance the field of medicine.