A Comprehensive Machine Learning Framework for the Exact Prediction of the Age of Onset in Familial and Sporadic Alzheimer’s Disease

Abstract

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, the comprehensive and sequential application of ML to provide an exact estimation of the actual ADAOO, instead of a high-confidence-interval ADAOO that may fall, remains to be explored. Here, we assessed the performance of ML algorithms for predicting ADAOO using two AD cohorts with early-onset familial AD and with late-onset sporadic AD, combining genetic and demographic variables. Performance of ML algorithms was assessed using the root mean squared error (RMSE), the R-squared (R2), and the mean absolute error (MAE) with a 10-fold cross-validation procedure. For predicting ADAOO in familial AD, boosting-based ML algorithms performed the best. In the sporadic cohort, boosting-based ML algorithms performed best in the training data set, while regularization methods best performed for unseen data. ML algorithms represent a feasible alternative to accurately predict ADAOO with little human intervention. Future studies may include predicting the speed of cognitive decline in our cohorts using ML.

Development of Improved Weighed Quantum Lion Optimization with Smooth Support Vector Machine for Alzheimer’s Disease

“…The CC splits into the right and left hemispheres with a fiber bundle called the CC. The right and left hemispheres could communicate with one another thanks to this CC [13]. The Healthy Control (HC) is a crucial part of the brain that lies beneath the medial temporal lobes; it is entirely in charge of forming new memories, retrieving existing memories is known as the "gateway of memories" [14][15].…”

“…Steps to follow SSVM features is for classifying AD (i) Calculate the input features Using Equation:(12)(12) (ii) Weighed Quantum estimation is performed Using Equation(13) …”

The Feasibility of Using Machine Learning to Classify Calls to South African Emergency Dispatch Centres According to Prehospital Diagnosis, by Utilising Caller Descriptions of the Incident

“…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 ].…”