Many important applications in Health Sciences and Biology have underlying datasets that have ambiguous class membership, that is, individual labels are difficult to establish. In such cases, many times, the training examples are easier to label as a group rather than at the instance level. Multiple Instance Learning (MIL) is a supervised learning strategy that addresses this labeling difficulty by employing training example given as positive and negative bags of instances. In this paper we describe a fuzzy variation of the MIL Diverse Density framework (FUMIL) based on ordered weighted geometric operator (OWG) and fuzzy complement operators. We apply FUMIL for early illness recognition of elderly living alone in their home. The available data consists of wireless non-wearable sensor values aggregated at hour level (instance) and ground truth (medical data) available at day level (bag). In our preliminary experiments FUMIL performed better than the traditional MIL framework.