# Prediction of Diabetes Complications Using Computational Intelligence Techniques
## 3 Solution Design and Implementation
### Design of the Solution  
Designing a machine learning solution for diabetic prediction involves several steps:  
Figure 2: Proposed model. **Data collection:** The initial step is to gather a large dataset that includes patient demographics, medical history, lab results, and other related information. This data can be obtained from electronic health records (EHRs) or other sources. **Data preprocessing:** The collected data must be cleaned and preprocessed to eliminate any inconsistencies or missing values. The data must also be transformed and scaled to ensure machine learning algorithms can use it. **Feature selection:** The next step is identifying the relevant features that will be used to train the machine learning model. This can include demographic information, lab results, and other related data. **Model selection:** After the features are selected, the next step is to select the appropriate machine learning model for the task. The model type will depend on the nature of the data and the desired outcome. For example, logistic regression or a decision tree model could be used for classification tasks. **Model training:** Once the model is selected, it needs to be trained on the collected and preprocessed data. The model will learn from the data and will be able to make predictions on new, unseen data. **Model evaluation:** After the model is trained, it needs to be evaluated using various metrics, such as accuracy, precision, and recall. The model's performance can be optimised by fine-tuning the hyperparameters and adjusting the features, if necessary. **Model deployment:** Once the model is trained and optimised, it can be deployed in a production environment. The model can be integrated with existing systems, such as EHRs, to make predictions about new patients and help with the early detection of diabetes. In Figure 3, the introduced system architecture is depicted. These are the steps involved in the prediction of diabetes:  
1. The system first receives the diabetes data set as input. 2. Based on the given symptoms, the diabetes predictor assists by predicting the presence of diabetes and generates the predicted results. 3. The diabetes monitor device assists in checking blood sugar levels and sends out alerts based on them. 4. The user receives the awareness message to know about their health status. The use of computational intelligence techniques for the prediction of diabetes complications offers several advantages, including:  
Figure 3: Proposed data architecture. **Early detection:** Computational intelligence techniques can identify patterns in the data that may not be apparent to human experts, allowing for the earlier detection of diabetes complications. Early detection can lead to more timely interventions and potentially prevent or delay the onset of complications. **Personalised treatment:** Predictive models can identify patients at higher risk of developing diabetes complications and tailor their treatment plans accordingly. This can lead to more personalised and effective care. **Improved patient outcomes:** By identifying patients at a higher risk of developing diabetes complications and providing early interventions, computational intelligence techniques can improve patient outcomes and reduce the overall burden of diabetes-related complications. **Efficient resource allocation:** By predicting which patients are at a higher risk of developing complications, healthcare resources can be allocated more efficiently, and patients can be prioritised for more intensive care. **Improved efficiency:** Computational intelligence techniques can process vast amounts of data quickly and efficiently, leading to improved healthcare delivery efficiency. **Reduced costs:** Early detection and timely interventions can potentially reduce the costs associated with diabetes complications by preventing or delaying the need for more intensive and costly treatments.