This study assessed the utility of the nationwide population-based administrative health data in predicting the future incidence of AD. Using machine learning, we predicted future incidence of AD with acceptable accuracy of 0.713 (in terms of AUC 0.781) in one-year prediction. The high accuracy of our models based on large nationwide samples may lend a support to the potential utility of the administrative data-based predictive model in AD. Despite of the limitations inherent to the administrative health data, such as the inability to directly ascertain clinical phenotypes, this study demonstrates its potential utility in AD risk prediction, when combined with data-driven machine learning.

Our model performance with AUC of 0.898, 0.775, and 0.725 in predicting baseline, subsequent one-year, and four-year incident AD is relatively accurate compared with the literature. In all-cause dementia risk prediction based on genetic (ApoE) or neuropsychological evaluations, MRI, health indices (diabetes, hypertension, lifestyle), and demographic (age, sex, education) variables, prior models show accuracy ranging from 0.5 to 0.78 in AUC (reviewed in ref. 18). Of note, no direct comparisons of our results with those studies should be made because of the differences in the study design (e.g., predicting AD risk in 20 years later), populations (e.g., non-Asians), and analytical model (e.g., linear models). Nevertheless, it should be noted that compared with the prior studies primarily based on targetted variables obtained from elaborate neuropsychological, genetic testing, or brain imaging, our approach is solely based on the administrative health data. This has important implications for the practical utility, in that it can provide an early indication of AD risk to clinicians prior to any assessments or tests. Together with existing screening tools (e.g., MMSE), this may assist deciding when to seek a further clinical assessment to a given patient in an individual-specific manner.

Comparing the models based on the sampled, balanced set and on the entire, unbalanced set showed small-to-moderate differences in model performance. For example, based on the RF model in predicting 0-year definite AD, the AUC’s are 0.887 and 0.898 in the unbalanced and balanced samples, respectively, showing a 1% increase. On the other hand, in predicting 4-year definite AD, the AUC’s are 0.662 and 0.725 in the unbalanced and balanced samples, respectively, showing a 9.5% increase. These results show trivial-to-moderate differences in model performance between balanced and balanced samples. However, we should point that, if one uses an algorithm capable of processing the temporal information among the clinical features, such as recurrent neural networks19, then using the entire data for scalable learning is likely to be beneficial.

Comparing the model performance across years, the 3-year prediction is less accurate than the 4-year prediction. This seems counter-intuitive at first, but our data shows that the length of data is greater in 4-year prediction than in 3-year prediction (Supplementary Table 2). We suspect that this difference in data availability may be a cause of the expected performance increase in later year prediction. This might be also related to the irregularity of the NHIS-NSC dataset due to changes in healthcare policy.

Our model detected the interesting clinical features associated with incident AD. The data-driven selection of features is consistent with risk factors found in the literature. A decrease in hemoglobin level was selected as the feature most strongly associated with incident AD. Indeed, anemia is known as an important risk factor for dementia20,21,22. A study using National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS), the NHIS health screening data in Korea, not only found that anemia was associated with dementia, but also revealed a dose-dependent relationship between anemia and dementia23. Likewise, our data-driven model shows the hemoglobin level as the most significant predictor. This finding has implications for public health because anemia is a modifiable factor. Given our finding and the consistent literature on the association between hemoglobin level and AD and other dementia, future research may investigate the biological pathway of anemia’s contribution to AD pathology and cognitive decline.

We also discovered a positive association between urine protein level and incident AD. In the NHIS-NSC, protein in urine is typically measured using dip sticks. Though this is not a quantitative measure of urine protein, it is useful as a screening method for proteinuria24,25. Literature shows an association between albuminuria and dementia26. Our finding suggests the potential utility of a urine test as part of the routine health check-up for AD risk prediction.

Four medications were also associated with incident dementia within top ten features. We found that Zotepine, Eperisone hydrochloride had a positive association and Nicametate Citrate and Tolfenamic acid had a negative association with incident AD. It is interesting that patients prescribed tolfenamic acid showed lower incidence of AD. This drug used in Korea for pain control in conditioner such as rheumatoid arthritis. It is known to lower the gene expression of Amyloid precursor protein 1(APP1) and beta-site APP cleaving enzyme 1(BACE1) by promoting the degradation of specificity protein 1(Sp1)27,28,29. As a potential modifier of tau protein, Tolfenamic acid is under investigation as a potential drug to prevent and modify the progression of AD30. The results of this study support the above experimental result and show that tolfenamic acid may be a potential anti-dementia medication.

Zotepine is an atypical antipsychotic drug with proven efficacy for treatment of schizophrenia. Our model showed the use of zotepine positively correlated with incident AD. There are two possible interpretations. Zotepine may have been used to treat behavioral and psychological symptoms of dementia (BPSD) before incident AD or diagnosis of AD31. Thus, the prescription of Zotepine may indicate early AD symptoms and, consequently, an increasing likelihood of incident AD. Alternatively, some studies indicate that individuals with schizophrenia may have an increased risk for the development of dementia32. Given this, it might be possible that incident AD is high in individuals with schizophrenic symptoms to whom Zotepine is prescribed. However, this alternative interpretation may be questionable considering that, in our model, the disease code of Schizophrenia has not been selected as an important feature. In either case, it should be noted that, though our results indicate a potential relationship between Zotepin and incident AD (likely reflecting the common practice in dementia), no causal relationship should be drawn.

Nicametate Citrate, a vasodilator, was also negatively associated with incident AD. This may be in line with the literature showing effects of vasodilators on increasing cognitive function and reducing the risk of vascular dementia, although the exact mechanism remains unclear33,34. Further research is required.

One of the limitations of this study is that diagnoses of AD in our database are not clinically ascertained. For example, there may be incorrect diagnoses or misdiagnoses of AD in the claim data. To mitigate this issue, we firstly confirmed the similar prediction results using two different definitions of incident AD, “probable AD” (based on AD disease codes) and “definite AD” (based on both AD disease codes and anti-dementia medication). Secondly, in South Korea, every elder with age 60 years old is required to have complementary dementia screening supported by the National Health Insurance Service at public healthcare centers, where individuals that high-risk for dementia get referred to physicians for further clinical examination. Such a system may help reduce false negative cases. Lastly, Korean health insurance system and policies support the reliability of the AD diagnoses. That is, the Health Insurance Review and Assessment Service of NHIS reviews and supervises the medical claims of AD medication. For example, it requires the following conditions to consider the insurance coverage of dementia medication: for donepezil and rivastigmine patches, MMSE (Mini-Mental State Examination) = <26 and CDR (Clinical Dementia Rating) = 1–3 or GDS (Global Deterioration Scale) = 3–7; for galantamine and rivastigmine capsules, MMSE = 10–26 and CDR = 1–2 or GDS = 3–5; for memantine, MMSE = < 20 and CDR = 2–3 or GDS = 4–7 (Supplementary Fig. 1). Thus, it is likely that individuals with records of receiving dementia medication meet strong diagnostic criteria. These aspects may alleviate potential validity issues of the AD diagnoses in the Korean administrative health data. Another limitation is that the features associated with incident AD do not indicate causality. Rather, this finding indicates a data-driven discovery from the large administrative data. This knowledge might be useful to generate new hypotheses, to confirm existing ones, or to compare relative importance in predicting incident AD considering large feature space. We believe this is a useful value of data-driven science.

In sum, this study lends support to a statistically meaningful detection of individuals with AD risk solely based on the administrative health data. Generalizability of our findings to independent data in other nations, ethnicities, and healthcare and insurance systems remains to be tested. If replicated, this study may further motivate the implementation of a system in clinical settings that could alarm a risk for AD, which may enable earlier and more accurate screening for subsequent clinical testing.