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Hard data analytics problems create better opportunities in analytics. This review cut across the boundaries of machine learning models for the eventual development of a successful precision medicine forecast model, different strategies for the integration of non-omics data and omics data, limitations and challenges in data integration, and future directions for the precision medicine forecasts. The literature also discusses non-omics data, diseases associated with air pollutants, and omics data. This information gives insight to the integrated data analytics and their application in future project implications. 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