Proposed machine learning models
dementia, Alzheimer’s diseaseFirst, MCI, cognitive impairment
Figure Our proposed overall architecture for the two models: the classification of MCI and CN uses an ensemble approach combining the three models of XGBoost, GB, and LGB, and the classification of MCI-AD and CN uses an ensemble approach combining the four models of XGBoost, GB, LGB, and AdaBoost. The balanced accuracy values from five models via fivefold cross-validation were used for cross-validation model weights to combine the five models. CN, cognitively normal; MCI, mild cognitive impairment; XGBoost, extreme gradient boosting; GB, gradient boosting; LBG, light gradient boosting; AdaBoost, adaptive boosting; AD, Alzheimer’s diseaseFirst, to classify MCI and CN, we used a light gradient boosting (LGB) model, which commonly trains data based on the gradient boosting principle. We applied an exhaustive search (brute-force search) and sequential model-based optimization (SMBO) to determine the optimum hyperparameters of the model. For LGB, we found the following optimum parameters: boosting parameter of gradient-based one-side sampling (GOSS), maximum depth of 4; learning rate, 0.0001; number of tree estimators, 100; fraction of observation, 0.5; fraction of columns, 0.1; and maximum number of leaves, 20. Based on the optimized models, we computed the probabilities of MCI and CN by averaging the outputs from the XGBoost, GB, and LGB models. Subsequently, we computed balanced accuracy values from the five models via fivefold cross-validation and used the accuracy values as cross-validation model weights. By weighting the cross-validation model weights to the probability values derived from the five models via fivefold cross-validation, we obtained the final probabilities for MCI and CN.Second, for the model to classify MCI-AD dementia and CN, we used an ensemble approach combining GB and LGB models. Here, one additional GB was combined with the model to classify MCI and CN. Similarly, we determined the optimal hyperparameters for each model. For GB, we found the following optimum parameters: maximum depth, 3; learning rate, 0.2; number of tree estimators, 100; and minimum number of observations, 4. For LGB, we found the following optimum parameters: boosting parameter of gradient-boosted decision trees (GBDT) maximum depth, 6; learning rate, 0.25; number of tree estimators, 100; fraction of observation, 0.6; fraction of columns, 0.6; and maximum number of leaves, 33. Based on the optimized models, we computed the probabilities of MCI-AD dementia and CN by averaging the outputs from the GB, and LGB models. Next, we computed balanced accuracy values from the five models via fivefold cross-validation and used the accuracy values as cross-validation model weights. By weighting the cross-validation model weights to the probability values derived from the five models via f-fold cross-validation, we obtained the final probabilities of MCI-AD dementia and CN.All processing steps were performed on a personal computer equipped with an Intel Core i7-12700F 4.9-GHz CPU, 512 GB of memory, and NVIDIA GEForce RTX 3080 Ti GPU. The models were implemented using Python (version 3.7.13) with TensorFlow-gpu (version 2.6.0), Keras (version 2.9.0), NumPy (version 1.19.5), Pandas (version 1.3.5), Matplotlib (version 3.5.1), and Scikit-learn (version 1.0.2).