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Background and Aim: Dementia is a group of symptoms characterized by memory loss, judgemental loss, forgetfulness. Dementia cannot be cured; but can be prevented. There are seven stages of Dementia, no cognitive decline being the first stage to very severe cognitive decline being the seventh stage. Healthy lifestyle is important for lower risk of Dementia. Smoking, consumption of alcohol, negative thinking, depression, stress, anxiety, lack of sleep, unhealthy diet causes Dementia. Diet plays a very important role in prevention of dementia; foods such as red meat, sugar, fatty foods can increase the risk of dementia whereas foods rich in fibres, fruits can reduce the risk of Dementia. The main aim of this study is to assess the knowledge about lifestyle and dementia. Materials and Methods: This is a cross sectional survey. 121 adolescents of Saveetha Dental College, Chennai participated in the survey which was conducted through Google forms in February 2021. The questionnaire consisted of 19 questions. The data was analyzed with the help of SPSS software and chi-square test was done for the correlation between gender and awareness. Chi-square test was done for the correlation of gender with awareness among the population. ‘p-value’ was calculated and value < 0.05 was considered as significant. Results: Out of that 62 were females and 59 were males. Females were more aware about healthy lifestyles and its connection with dementia than males. 92.56% of people were aware that physical activity promotes a healthy brain. 69.2% agreed that depression can increase risk of Dementia. Majority of them agreed that physical exercise can promote a healthy brain. Conclusion: This study revealed that lifestyle is highly associated with dementia among adolescents. Healthy lifestyle leads to lower risk of dementia even if there’s high genetic risk. Females were aware about the diet which is healthy for lower risk of dementia and more smoking affects the memory. However, most of the people were neutral about the relation between BMI and Dementia.

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Subgroup discovery is a descriptive and exploratory data mining technique to identify subgroups in a population that exhibit interesting behavior with respect to a variable of interest. Subgroup discovery has numerous applications in knowledge discovery and hypothesis generation, yet it remains inapplicable for unstructured, high-dimensional data such as images. This is because subgroup discovery algorithms rely on defining descriptive rules based on (attribute, value) pairs, however, in unstructured data, an attribute is not well defined. Even in cases where the notion of attribute intuitively exists in the data, such as a pixel in an image, due to the high dimensionality of the data, these attributes are not informative enough to be used in a rule. In this paper, we introduce the subgroup-aware variational autoencoder, a novel variational autoencoder that learns a representation of unstructured data which leads to subgroups with higher quality. Our experimental results demonstrate the effectiveness of the method at learning subgroups with high quality while supporting the interpretability of the concepts.Keywords Subgroup Discovery • Unstructured Data • Pattern Mining Despite numerous works in the literature, all subgroup discovery techniques suffer from a limitation -they are only applicable to structured data, i.e., data with well-defined attributes. A typical instance of this would be data found relational databases. However, in many settings, the data to be dealt with is unstructured and potentially high dimensional such as images, videos, text documents, and time series. Subgroups are defined through a conjunction of attribute and value pairs. For high dimensional data, such as an image, this implies that the subgroups will be defined based on the

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