[en] Frailty is a geriatric medical condition that is highly associated with age and age-related diseases. The multidimensional consequences of frailty are heavily impacting the quality of life, and will inevitably increase the burden on healthcare systems in the future. Most importantly, the lack of a universal standard to describe, diagnose, or let alone treat frailty, is further complicating the situation in the long-term. Nowadays, more and more frailty assessment tools are being developed on a regional and institutional basis, which is continuing to drive the heterogeneity in the characterization of frailty further apart. Gaining better insights into the underlying causes and pathophysiology of frailty, and how it is developing in patients is, therefore, required to establish strong and accurately tailored response schemes for frail patients, where currently only symptoms are treated. Thus, in this study, we deployed machine learning-based classification and optimization techniques to predict frailty in elderly people aged 65 or above from the Berlin Aging Study II (BASE-II, n=1512, frail=484) and revealed some of the most informative biomedical information to characterize frailty, including new potential biomarkers. Frailty in BASE-II was measured by the Fried et al. 5-item frailty index, composed of the clinical variables grip strength, weight loss, exhaustion, physical activity, and gait. The level of frailty in BASE-II was adapted for binary classification purposes by merging the pre-frail and frail levels as frail. A configurable in-house pipeline was developed for pre-processing the clinical data and predicting the target disease by deploying Support Vector Machines Classification. The most informative and essential subgroup of clinical measurements with regards to frailty was investigated by re-optimizing an initially full data-driven model by sequentially leaving out one subgroup. The best prediction power was yielded with resampling and dimensionality reduction techniques using the F-beta-2 score, and was further improved by adding one item of the Fried et al. frailty index. Furthermore, differences between the gender in the data set led to the investigation of gender-specific model configurations, followed by re-optimizations. As a result, we were able to specifically increase the predictive power in gender-specific groups, and will simultaneously emphasize on the differences between the most informative clinical biomarkers as well as the most essential subgroups for mixed and gender-specific BASE-II. The results herein suggest that a combination of the detected easy-to-obtain biomedical information on frailty risk factors together with one Fried et al. phenotype information provided by i.e., smart wearable devices (gait, grip strength, …) could significantly improve the frailty prediction power in mixed and gender-specific clinical cohort data.