presents the performance measures for collecting ML algorithms used to pre- dict AOO in individuals of the sAD cohort. The training and data sets consisted of 40 and

presents the performance measures for collecting ML algorithms used to pre- dict AOO in individuals of the sAD cohort. The training and data sets consisted of 40 and

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Machine learning (ML) algorithms are widely used to develop predictive frameworks. Accurate prediction of Alzheimer’s disease (AD) age of onset (ADAOO) is crucial to investigate potential treatments, follow-up, and therapeutic interventions. Although genetic and non-genetic factors affecting ADAOO were elucidated by other research groups and ours,...

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... the glmnet and glmboost algorithms, which outperform the other alternatives when predicting ADAOO for unseen data, the most important predictors are the genetic variants APOE-rs7412, FCRL5-rs16838748, GRP20-rs36092215, IFI16-rs62621173, AOAH-rs12701506, and PYNLIP-rs2682585, followed by years of education (Figure 1c, center; Figure 1c, right). Table 3 presents the performance measures for collecting ML algorithms used to predict AOO in individuals of the sAD cohort. The training and data sets consisted of 40 and 14 individuals, respectively. ...
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... the svmLinear algorithm seems to be a better alternative than xgbLinear algorithm. On the other hand, when evaluating the performance of these ML algorithms for the testing data set, the lasso outperforms the other alternatives in terms of the RMSE and R 2 , while the glmnet algorithm does so in terms of the MAE (Table 3). In contrast, these ML algorithms are strong learners. ...
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... the testing data set, the glmboost, xgbTree, rf, svmRadial, and bstTree algorithms belong to class 1 ( Figure 2b, yellow); svmPoly, svmLinear, svmLinear2, lasso, and glmnet algorithms belong to class 2 ( Figure 2b; red); and treebag, rpart, rpart1SE, rpart2, and qrf constitute class 3 (Figure 2b; blue). Overall, the best performing algorithms are grouped into class 2 for both the training and testing data sets; the xgbLinear algorithm outperforms all other alternatives for the training data set (Table 3 and Figure 2a), while the lasso and glmnet algorithms seem to be the best options for unseen data (Table 3 and Figure 2b). Figure 2c depicts variable importance plots for the svmLinear, lasso, and glmnet algorithms. ...
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... the testing data set, the glmboost, xgbTree, rf, svmRadial, and bstTree algorithms belong to class 1 ( Figure 2b, yellow); svmPoly, svmLinear, svmLinear2, lasso, and glmnet algorithms belong to class 2 ( Figure 2b; red); and treebag, rpart, rpart1SE, rpart2, and qrf constitute class 3 (Figure 2b; blue). Overall, the best performing algorithms are grouped into class 2 for both the training and testing data sets; the xgbLinear algorithm outperforms all other alternatives for the training data set (Table 3 and Figure 2a), while the lasso and glmnet algorithms seem to be the best options for unseen data (Table 3 and Figure 2b). Figure 2c depicts variable importance plots for the svmLinear, lasso, and glmnet algorithms. ...
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... evaluating several ML-based predictive algorithms for ADAOO in individuals suffering from the most aggressive form of AD ( Figure 1 and Table 2) and in individuals with sporadic AD (Figure 2 and Table 3), we identified that the glmboost and glmnet algorithms perform best for predicting ADAOO in unseen data for each cohort, respectively. ...
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... ML-based predictive models showed promising results that can be easily extended to the clinical setting [98]. In particular, the glmboost algorithm in E280A PSEN1 AD yielded MAE values below 4% and RMSE values of ~4 (Table 2), while the glmnet algorithm yielded MAE values below 1% and RMSE values < 1 in sAD ( Table 3), suggesting that predicting AOO in these cohorts is feasible. Using these ML-based ADAOO predictive models, AD diagnosis could be made earlier, and potential treatments are provided long before symptoms begin to appear. ...

Citations

... Manual transcription is heavily relied on since artificial intelligence driven speech-to-text applications are not sensitive to South African dialects and only recognise pronunciations from higher-income countries. Machine learning (ML) and natural language processing (NLP) have been repeatedly used to boost productivity [8][9][10] and are, thus, viable solutions to manual transcription. A recent study found that ML and NLP can be applied to the emergency department triage, and noted to predict patient disposition with a high level of accuracy [11]. ...
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Chapter
Alzheimer’s Disease (AD) is a neurodegenerative disorder primarily characterized by deteriorating cognitive functions. In 2016 an estimated 40 million people were diagnosed with AD, and the expectation for 2050 is 131 million. Therefore, healthcare systems require detecting and confirming AD at its different stages to provide adequate and accurate treatments. Recently, Machine Learning (ML) models have been used to classify AD’s stages. It has become a priority to develop a framework for AD’s stages detection based on ML and imputation methods capable of handling datasets with missing values while providing high accuracy. We propose a ML computational framework that integrates data processing, feature selection, imputation methods and 5 different ML models. The performance of the proposed framework has been evaluated using the main metrics for classification problem; accuracy, F1- score, recall, and precision. As a results of the proposed process, our framework classifies the AD’s onsets with an accuracy of 99%.KeywordsMachine learning techniquesAlzheimer’s diseaseClassification problemMissing data