Abstract
BackgroundAlzheimer’s disease has become a public health crisis globally due to its increasing incidence. The purpose of this study was to establish an early warning model using artificial neural network (ANN) for early diagnosis of AD and to explore early sensitive markers for AD.MethodsA population based nested case-control study design was used. 89 new AD cases with good compliance who were willing to provide urine and blood specimen were selected from the cohort of 2482 community-dwelling elderly aged 60 years and over from 2013 to 2016. For each case, two controls living nearby were identified. Biomarkers for AD in urine and blood, neuropsychological functions and epidemiological parameters were included to analyze potential risk factors of AD. Compared with logistic regression, k-Nearest Neighbor (kNN) and support vector machine (SVM) model, back-propagation neural network of three-layer topology structures was applied to develop the early warning model. The performance of all models were measured by sensitivity, specificity, accuracy, positive prognostic value (PPV), negative prognostic value (NPV), the area under curve (AUC), and were validated using bootstrap resampling.ResultsThe average age of AD group was about 5 years older than the non-AD controls (P < 0.001). Patients with AD included a significantly larger proportion of subjects with family history of dementia, compared with non-AD group. After adjusting for age and gender, the concentrations of urinary AD7c-NTP and aluminum in blood were significantly higher in AD group than non-AD group (2.01 ± 1.06 vs 1.03 ± 0.43, 1.74 ± 0.62 vs 1.24 ± 0.41 respectively), but the concentration of Selenium in AD group (2.26 ± 0.59) was significantly lower than that in non-AD group (2.61 ± 1.07). All the models were established using 18 variables that were significantly different between AD patients and controls as independent variables. The ANN model outperformed the other classifiers. The AUC for this ANN was 0.897 and the model obtained the accuracy of 92.13%, the sensitivity of 87.28% and the specificity of 94.74% on the average.ConclusionsIncreased risk of AD may be associated with higher age among senior citizens in urban communities. Urinary AD7c-NTP is clinically valuable for the early diagnosis. The established ANN model obtained a high accuracy and diagnostic efficiency, which could be a low-cost practicable tool for the screening and diagnosis of AD for citizens.
Highlights
Alzheimer’s disease has become a public health crisis globally due to its increasing incidence
K-nearest neighbor, support vector machine (SVM) model For the purpose of testing the advantage of NN algorithm, logistic regression model, k-Nearest Neighbor and support vector machine (SVM), were applied using the same 18 variables that were significantly different between Alzheimer’s disease (AD) patients and controls as independent variables to make a comparison with artificial neural network (ANN)
Biomarkers in urine and blood The concentrations of urinary AD7c-NTP and aluminum in blood were significantly higher in AD group than non-AD group (2.01 ± 1.06 vs 1.03 ± 0.43, 1.74 ± 0.62 vs 1.24 ± 0.41 respectively)
Summary
Alzheimer’s disease has become a public health crisis globally due to its increasing incidence. With the enlarging proportion of aging population, the incidence of Alzheimer’s disease (AD) has been increasing, which no doubt becomes a public health crisis globally [1]. AD will seriously affects the quality of life of patients, and there are no ideal drugs or methods for clinical treatment [2]. In 2015, the time of care provided to patients with AD and other dementias was nearly 18.1 billion hours which valued more than $221 billion [1]. Alzheimer’s disease is becoming the fastest growing fatal disease and at least 9.5 million patients have been diagnosed by far. 1 million new cases will be found every year and the number of new cases is increasing year by year
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