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Introduction and motivation Prevention is one of the most important pillars of public health. The Institute of Medicine (IOM) estimates that missed prevention opportunities cost the US $55 billion every year, and an estimate of ~ 30 cents on every healthcare dollar. In total, the healthcare system squanders $750 billion a year [1]. Accordingly, amongst all the countries of the OECD (the Organization for Economic Cooperation and Development), the US has the Abstract Data-driven healthcare policy discussions are gaining traction after the Covid-19 outbreak and ahead of the 2020 US presidential elections. The US has a hybrid healthcare structure; it is a system that does not provide universal coverage, albeit few years ago enacted a mandate (Affordable Care Act-ACA) that provides coverage for the majority of Americans. The US has the highest health expenditure per capita of all western and developed countries; however, most Americans don't tap into the benefits of preventive healthcare. It is estimated that only 8% of Americans undergo routine preventive screenings. On a national level, very few states (15 out of the 50) have above-average preventive healthcare metrics. In literature, many studies focus on the cure of diseases (research areas such as drug discovery and disease prediction); whilst a minority have examined data-driven preventive measures-a matter that Americans and policy makers ought to place at the forefront of national issues. In this work, we present solutions for preventive practices and policies through Machine Learning (ML) methods. ML is morally neutral, it depends on the data that train the models; in this work, we make the case that Big Data is an imperative paradigm for healthcare. We examine disparities in clinical data for US patients by developing correlation and imputation methods for data completeness. Non-conventional patterns are identified. The data lifecycle followed is methodical and deliberate; 1000+ clinical, demographical, and laboratory variables are collected from the Centers for Disease Control and Prevention (CDC). Multiple statistical models are deployed (Pearson correlations, Cramer's V, MICE, and ANOVA). Other unsupervised ML models are also examined (K-modes and K-prototypes for clustering). Through the results presented in the paper, pointers to preventive chronic disease tests are presented, and the models are tested and evaluated.

Background: We describe a DNA methylation assay, named MPap test, using cervical scraping as an alternative technique for endometrial cancer detection. Methods: A multicenter hospital-based, two-stage validation study was conducted to validate the cancer detection performance of the MPap test. The MPap value was determined from the DNA methylation status of two genes (BHLHE22, CDO1) and combined with two other clinical variables (age, BMI). The cutoff threshold of the MPap value was established in stage 1 and validated in stage 2. A total of 592 women with abnormal uterine bleeding were enrolled from five medical centers throughout Taiwan. Results: In stage 1, the sensitivity, specificity, and positive and negative predictive values of the MPap test for detecting endometrial cancer were 92.9%, 71.5%, 39.8%, and 98.0%, respectively. These values were validated in stage 2, being 92.5%, 73.8%, 40.2%, and 98.1%. Moreover, MPap outperformed transvaginal ultrasound in sensitivity and negative predictive values for detecting endometrial cancer. When we applied the algorithm for triage of endometrial cancer detection by MPap in the Taiwan National Health Insurance dataset, we found that it may reduce invasive procedures by 69~73%. Conclusions: MPap may provide a feasible alternative for endometrial cancer detection and can be considered as a triage test to reduce unnecessary invasive procedures.

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Window of implantation (WOI) genes have been comprehensively identified at the single cell level. DNA methylation changes in cervical secretions are associated with in vitro fertilization embryo transfer (IVF-ET) outcomes. Using a machine learning (ML) approach, we aimed to determine which methylation changes in WOI genes from cervical secretions best predict ongoing pregnancy during embryo transfer. A total of 2708 promoter probes were extracted from mid-secretory phase cervical secretion methylomic profiles for 158 WOI genes, and 152 differentially methylated probes (DMPs) were selected. Fifteen DMPs in 14 genes (BMP2, CTSA, DEFB1, GRN, MTF1, SERPINE1, SERPINE2, SFRP1, STAT3, TAGLN2, TCF4, THBS1, ZBTB20, ZNF292) were identified as the most relevant to ongoing pregnancy status. These 15 DMPs yielded accuracy rates of 83.53%, 85.26%, 85.78%, and 76.44%, and areas under the receiver operating characteristic curves (AUCs) of 0.90, 0.91, 0.89, and 0.86 for prediction by random forest (RF), naïve Bayes (NB), support vector machine (SVM), and k-nearest neighbors (KNN), respectively. SERPINE1, SERPINE2, and TAGLN2 maintained their methylation difference trends in an independent set of cervical secretion samples, resulting in accuracy rates of 71.46%, 80.06%, 80.72%, and 80.68%, and AUCs of 0.79, 0.84, 0.83, and 0.82 for prediction by RF, NB, SVM, and KNN, respectively. Our findings demonstrate that methylation changes in WOI genes detected noninvasively from cervical secretions are potential markers for predicting IVF-ET outcomes. Further studies of cervical secretion of DNA methylation markers may provide a novel approach for precision embryo transfer.