The early prediction of AD evolution based on Trusted-LGBM

In order to achieve the purpose of early diagnosis of Alzheimer’s disease, this study uses machine learning technology to process 14,659 original data from the public data set ADNI. First use the KNN algorithm to fill in the null value. Then a feature transformation method is proposed to transform regression problems into classification problems. Then this study calculates the trust level of each sample based on the feature correlation and the weight of null value, and proposes an improved LightGBM algorithm-Trusted-LGBM. Finally, the prediction results of the Trusted-LGBM algorithm are compared with SVM, XGBoost, and the unimproved LightGBM algorithm. The experimental results show that the Trusted-LGBM algorithm proposed by this research has a higher Fl-score (0.784) and AUC (0.91). It can be seen that the method proposed in this study can more effectively support the early diagnosis of Alzheimer’s disease.