Household finances are being threatened by unprecedented social and economic upheavals, including an aging society and slow economic growth. Numerous researchers and practitioners have provided guidelines for improving the financial statuses of households; however, handling the heterogeneous households remains nontrivial. In this study, we propose a new data-driven framework for the financial health of households to address the needs for diagnosing and improving financial health. This research extends the concept of healthcare to household finance. We develop a novel deep learning-based diagnostic model for estimating household financial health risk scores from real-world household balance sheet data. The proposed model can successfully manage heterogeneity of households by standardizing the input data within carefully designed cohorts and adjusting the risk information for each variable. This is the first attempt to establish the concept of household financial health under conditions of heterogeneity and to provide a data-driven diagnostic model by grafting deep learning techniques.