# LSTM DSS Automatism and Dataset Optimization for Diabetes Prediction+
###### Abstract  
The paper is focused on the application of Long Short-Term Memory (LSTM) neural network enabling patient health status prediction focusing the attention on diabetes. The proposed topic is an upgrade of a Multi-Layer Perceptron (MLP) algorithm that can be fully embedded into an Enterprise Resource Planning (ERP) platform. The LSTM approach is applied for multi-attribute data processing and it is integrated into an information system based on patient management. To validate the proposed model, we have adopted a typical dataset used in the literature for data mining model testing. The study is focused on the procedure to follow for a correct LSTM data analysis by using artificial records (LSTM-AR-), improving the training dataset stability and test accuracy if compared with traditional MLP and LSTM approaches. The increase of the artificial data is important for all cases where only a few data of the training dataset are available, as for more practical cases. The paper represents a practical application about the LSTM approach into the decision support systems (DSSs) suitable for homecare assistance and for de-hospitalization processes. The paper goal is mainly to provide guidelines for the application of LSTM neural network in type I and II diabetes prediction adopting automatic procedures. A percentage improvement of test set accuracy of 6.5% has been observed by applying the LSTM-AR- approach, comparing results with up-to-date MLP works. The LSTM-AR- neural network can be applied as an alternative approach for all homecare platforms where not enough training sequential dataset is available. LSTM; DSS; diabetes prediction; homecare assistance information system; muti-attribute analysis; artificial training dataset +
Footnote †: This work is an extended version of our research published in 2018 at the conference \"AEIT 2018 International Annual Conference\" held in Bari, Italy, 3-5 October 2018.  
the use of certified smart sensors transmitting data in a cloud network could remotely control the patients at home [17]. The sensor enabling homecare assistance can be implemented into a more complex information hospital system embedding automatic alerting conditions based on different risk levels [18]. In this direction, KNIME workflows can be easily interfaced as a Graphical User Interface (GUI) to the control room information system, thus allowing the connectivity with big data systems and timing data process by cron job run managing the multilayer perceptron (MLP) ANN analyses [19]. In Figure 1a is illustrated an example information system basic architecture of the MLP ANN network linked with the control room and big data system for homecare assistance [19], and in Figure 1b schematized the related KNIME workflow by distinguishing the data process phases such as time delay for the workflow execution, python node enabling data input access, data pre-processing, data model processing, and reporting [19]. The ANN model implemented by workflows with objects are user friendly but cannot be easily implemented into Enterprise Resource Planning (ERP) software. For this purpose it is preferable to embed ANN scripts directly into the ERP framework, thus facilitating the DSS platform implementation and execution. For this purpose, it is preferable to adopt the python language, which can be easily embedded in different ERP frameworks. In previous literature the Long Short-Term Memory (LSTM) neural network has been adopted for predictive diagnostics, assuring good performance results [20; 21; 22]. Following this direction, the traditional ANN MLP prediction network, applied in the work [19] using a single attribute labeling, has been substituted by an LSTM neural network based on a multi-attribute analysis. The passage from the workflow implementation to the python script is necessary in order to properly design a neural network embedded into an ERP platform, potentially enabling data processing automatiss.