Importance

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Abstract

In one aspect, a computerized method for generation of datasets for machine learning models is used to determine a geo-location-based LifeScore. The method includes the step of implementing a machine learning modeling process to combine a healthcare attribute variable as an outcome or target variable as a function of lifestyle behavioral attributes, socio-economic, demographic, healthcare provisional, socio-networking, physical-environmental and other locality specific feature variables to generate a life outcome model of a locality. The method includes the step of updating the life outcome model based on a combination of location specific mortality, life expectancy, self assessed poor health variable value, a poor physical health days variable value, a frequent physical distress variable value. The method includes the step of using a set of socio-economic wellbeing principal components on the independent, driving side of features to update the life outcome model. The method includes the step of generating a community well-being index of the locality to update the life outcome model. The method includes the step of using a set of variables that measure collective efficacy or social cohesion to update the life outcome model. The method includes the step of using a specified community, institutional and family index to update the life outcome mode. The method includes the step of using the life outcome model to generate a LifeScore.

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