An app for predicting patient dementia classes using convolutional neural networks (CNN) and artificial neural networks (ANN): Comparison of prediction accuracy in Microsoft Excel

Abstract

Background: Dementia is a progressive disease that worsens over time as cognitive abilities deteriorate. Effective preventive interventions require early detection. However, there are no reports in the literature concerning apps that have been developed and designed to predict patient dementia classes (DCs). This study aimed to develop an app that could predict DC automatically and accurately for patients responding to the clinical dementia rating (CDR) instrument. Methods: A CDR was applied to 366 outpatients in a hospital in Taiwan, with assessments on 25 and 49 items endorsed by patients and family members, respectively. The 2 models of convolutional neural networks (CNN) and artificial neural networks (ANN) were applied to examine the prediction accuracy based on 5 classes (i.e., no cognitive decline, very mild, mild, moderate, and severe) in 4 scenarios, consisting of 74 (items) in total, 25 in patients, 49 in family, and a combination strategy to select the best in the aforementioned scenarios using the forest plot. Using CDR scores in patients and their families on both axes, patients were dispersed on a radar plot. An app was developed to predict patient DC. Results: We found that ANN had higher accuracy rates than CNN with a ratio of 3:1 in the 4 scenarios. The highest accuracy rate (=93.72%) was shown in the combination scenario of ANN. A significant difference was observed between the CNN and ANN in terms of the accuracy rate. An available ANN-based app for predicting DC in patients was successfully developed and demonstrated in this study. Conclusion: On the basis of a combination strategy and a decision rule, a 74-item ANN model with 285 estimated parameters was developed and included. The development of an app that will assist clinicians in predicting DC in clinical settings is required in the near future.

Detecting dengue fever in children using online Rasch analysis to develop algorithms for parents: An APP development and usability study

“…DF classification was compared with 4 prediction models, including CNN, ANN, KNN, and LR. In studies, [30][31][32][33]68] CNN and ANN have been mentioned in conjunction with Microsoft (MS) Excel modules. Step 1: Arranging data in MS Excel (in panel A of Fig.…”

“…[70][71][72] The chi-square test was employed to evaluate heterogeneity among the variables, and for each study, forest plots, also known as confidence interval plots, were created to illustrate the effect estimates and their corresponding confidence interval. Medicine DF) and normalized data (mean = 0, SD = 1) were compared to assess model ACC and stability among several algorithms, including CNN, ANN, KNN, LR, and others obtained from WEKA software (University of Waikato, Wellington, New Zealand), [73] such as Support Vector Machines, [74] LIBSVM, [75] BauesNET, Naive Bayes, [76] Random Forest Classification, [77] REPTtree, [78] LR, [79] ANN, [80] and CNN [30][31][32][33]68] ; see the reference. [58,69] An acceptable model ACC and stability for DF prediction was determined as AUC >=0.8 in the training set and AUC >=0.7 in the testing set.…”

Hospital Management Practice of Combined Prediction Method Based on Neural Network

“…At the same time, this ensures the development of intelligent hospital management by developing an automatic fluid management system that realizes automatic control of infusion flow and real-time monitoring of the patient's blood volume, which solves the problems of duplicated work and waste of human, material, and financial resources (Yang et al, 2023). Ho et al (2023) have studied the application of AI in hospital personnel management, equipment management, and environmental monitoring and concluded that AI assists doctors in seeing patients, puts higher requirements on them, stimulates doctors' innovation ability, provides advanced technical support for hospital management, and improves the hospital management mode. Establishing a hospital environment information platform creates a good ward and medical environment, ensures medical quality, and improves management efficiency (Panja et al, 2023).…”