We discuss the practical employment of a machine learning (ML) technique within AI for a social good application. We present an application for elderly adult dementia onset prognostication. First, the paper explains our encouraging preliminary study results of EEG responses analysis using a signal complexity measure of multiscale entropy (MSE) in reminiscent interior working memory evaluation tasks. Then, we compare shallow and deep learning machine learning models for a digital biomarker of dementia onset detection. The evaluated machine-learning models succeed in the most reliable median accuracies above 80% using random forest and fully connected neural network classifiers in automatic discrimination of normal cognition versus a mild cognitive impairment (MCI) task. The classifier input features consist of MSE patterns only derived from four dry EEG electrodes. Fifteen elderly subjects voluntarily participate in the reported study focusing on EEG-based objective dementia biomarker advancement. The results showcase the essential social advantages of artificial intelligence (AI) application for the dementia prognosis and advance ML for the subsequent use for simple objective EEG-based examination.Clinical relevance- This manuscript introduces an objective biomarker from EEG recorded by a wearable for a plausible replacement of a mild cognitive impairment (MCI) evaluation using usual biased paper and pencil examinations.