###### Abstract  
When dementia is diagnosed, it is most often already past the point of irreversible neuronal deterioration. Neuropsychological tests are frequently used in clinical settings; however, they must be administered properly and are oftentimes conducted after cognitive impairment becomes apparent or is raised as a concern by the patient or a family member. It would be beneficial to develop a non-invasive system for approximating cognitive scores which can be utilized by a general practitioner without the need for cognitive testing. To this end, gait, visual, auditory, postural, and olfactory function parameters, reported history of illness, and personal habits were used to train an elastic-net regression model in predicting the cognitive score. Community-dwelling men (N = 104) above the age of sixty-five participated in the current study. Both individual variables and principal components of the motor and sensory functions were included in the elastic-net regression model, which was trained on 70% of the dataset. The years of education, limits of stability testing time, regular ophthalmological exams, postural testing time principal component, better ear score on the sentence recognition test, and olfactory discrimination score largely contributed to explaining over 40% of the variance in the cognitive score. cognitive function; elastic-net regression; gait; posture; vision; hearing; olfaction +
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these tests are usually performed only after cognitive decline is presented as a concern by the patient or a caregiver. This results in fewer patients being screened for cognitive decline and a diagnosis in the mild or moderate stage of the disease, by which time a significant amount of neuronal injury has occurred [5]. Developing a non-invasive system that can approximate a cognitive score without the use of neuropsychological testing could allow practitioners to assess their patients quickly and refer them for further testing if needed. To achieve this, it is necessary to investigate possible biomarkers that can be obtained using non-invasive methods that are sensitive to differences in cognitive functioning. One possible method is the use of motor and cognitive function features, such as gait, visual, auditory, postural, and olfactory function features, as previous research has identified the existence of robust correlations between these functions and cognitive performance. Gait function and its relation to mild cognitive impairment and dementia have been investigated by numerous studies. The majority of the studies investigated spatiotemporal parameters and gait variability in single- and double-task settings [6]. Many studies reported gait speed and step and stride lengths as differentiating factors between AD and normal cognition or between AD and MCI [7; 8; 9; 10]. Some studies determined that there is a potential for detecting AD and MCI using continuous gait monitoring [11] or by training machine learning models on gait data [12]. Compared to the studies regarding gait and cognition, there are much fewer studies regarding the relationship between cognition and postural control and it has been noted that further investigation is necessary [13]. The few existing studies reported significant correlations between cognitive performance and postural parameters [14; 15]. Various sensory parameters have also been shown to have a robust relationship with cognition. A recent study concluded that all types of measured visual impairment were associated with a higher dementia prevalence [16], and meta-analysis research confirmed the consistency of the association [17]. Additionally, a 9-year longitudinal study found better visual ability to be associated with better cognitive performance and slower rates of decline on both vision-dependent and vision-independent cognitive tests [18]. In the case of auditory ability, hearing loss was identified as a risk factor for dementia [19; 20].