Application of MLOps in Prediction of Lifestyle Diseases
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Abstract
Our health is the consequence of a combination of intricate and stabilized interactions between genetic elements, microbiome configuration, environmental impulses, and lifestyle habits. Knowledge of their contributions, as well as the complex network that connects them, is essential to analyse the underlying mechanisms and the onset of many diseases and can provide crucial information on their prevention, diagnosis, and remedy. We have seen advancements in data analytic methods to discover valuable patterns by analysing great amounts of non-standard, heterogeneous, unstructured, and incomplete healthcare data. Not only does it help in decision making, but also forecasting. With the limited accuracy identified in machine learning models, we have implemented a process framework of Machine Learning Operations (MLOps) to develop a robust and collaborative platform for streamlining data and process integration and synergy through the automation of retaining, testing and deployment. In the current research work, a survey was made among the various stages of the employed individuals to understand their lifestyles and the related disorders. The data recorded was analysed using the Machine Learning approach.
We identified 6 key factors that form a healthy lifestyle- diet, education, technological usage, sleep, exercise, and recreational activities. A corporate employee’s lifestyle tends to become very haphazard. The sleep cycle changes frequently. There are a lot of unhealthy eating habits- be it eating out or eating at odd hours. Apart from this, there is too much physical and mental stress. The mental burden can come in many ways. There are waves of emotional imbalances. There is pressure to look a certain way to fit into society. To do certain things and follow certain habits to “behave appropriately”. Relationships of lifestyle shifts with academic achievements are interrelated. The concept of lifestyle promotion has to be incorporated in the SOP to ensure they have good lifestyle habits. With the increase in social media addiction, the value for sports and recreation has seen a major drop. Regular exercise is a must to keep the mind and body active and healthy. Creating awareness from an early age helps to promote a better lifestyle.
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Predictive methods, Machine Learning, Lifestyle disorders, healthcare data
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