Predictive self-organizing neural networks for in-home detection of Mild Cognitive Impairment - TEH, Seng Khoon (Institutional Knowledge at Singapore Management University, 2022)
In-home sensing of daily living patterns from older adults coupled with machine learning is a promisingapproach to detect Mild Cognitive Impairment (MCI), a potentially reversible condition with early detectionand appropriate intervention. However, the number of subjects involved in such real-world studies istypically limited, posing the so-called small data problem to most predictive models which rely on a sizablenumber of labeled data. In this work, a predictive self-organizing neural network known as fuzzy AdaptiveResonance Associate Map (fuzzy ARAM) is proposed to detect MCI using in-home sensor data collected from aunique Singapore cross-sectional study. Specifically, mean and standard deviation of nine in-home behavioralattributes of 49 subjects over two months were derived for each subject from the raw sensor data. We firstapplied fuzzy ARAM to the 49-subject data set with missing data, and achieved a F1-score of 58.3% to detectMCI from cognitive healthy. To eliminate the effect of missing data, we next conducted our study using an evensmaller 25-subject data set with no missing values, of which fuzzy ARAM achieved a F1-score of 63.6%. Toderive concise rules for prediction and interpretation, antecedent pruning was subsequently employed. For the25-subject data set, the F1-score improved to 76.2%, while the symbolic IF-THEN rules revealed that behaviormetrics such as variation of forgetfulness and sleep contained notable predictive utility. Compared with SupportVector Machines (SVM), Decision Tree, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN)and Long Short-Term Memory (LSTM), our benchmark experiments show that fuzzy ARAM provided the highestpredictive performance and yielded unique rules for MCI detection. These results demonstrate the potential offuzzy ARAM to detect MCI using in-home monitoring sensor data.