# Diabetes Monitoring System in Smart Health Cities Based on Big Data Intelligence
## 4 Experimental Results and Analysis
### Analysis and Discussion  
Diabetes mellitus is considered one of the risky diseases. Obesity, age, sedentary lifestyle, living way, inherited diabetes, poor diet, high blood pressure, and many more habits are the main causes of diabetes. In this study, we used KNN, DT classifier, deep learning using MLP, SVM, RF, AdaBoost classifier and logistic regression classification algorithms as supervised learning. Unsupervised learning methods are mostly used for dimensionality lowering. Undue attributes in the diabetes dataset are hinder the accuracy of the classifier.We can have the integration of supervised and unsupervised learning for the best prediction of diabetes. Table 5 reviews the summary of major findings of diabetes prediction from the literature. The results of the proposed research suggest that the AdaBoost classifier is more suitable for the detection of diabetes in healthcare. There are several limitations of using machine learning (ML) in diabetes prediction in health-monitoring work:  
1. Data bias: ML models are only as good as the data they are trained on, and if the data are biased, the model will also be biased. This can lead to inaccurate predictions and poor performance, particularly for underrepresented groups. 2. Lack of interpretability: Many ML models, particularly deep learning models, are considered \"black boxes\" and can be difficult to interpret. This can make it challenging for researchers and healthcare providers to understand how the model is making its predictions and identify potential errors. 3. Limited generalizability: ML models are trained on specific datasets, and may not perform well on new or unseen data. This can be a significant limitation in healthcare, where data are often collected from diverse populations with unique characteristics. 4. Lack of domain knowledge: Many machine learning algorithms and models are complex and require a deep understanding of the domain and data to be used effectively. 5. Limited data availability: In some cases, there may not be enough data available to train effective ML models, which can be a significant limitation in healthcare, where data are often collected from diverse populations with unique characteristics. 6. Lack of explainability and transparency: ML models are often considered \"black boxes\" which can be difficult for healthcare practitioners and patients to understand the decision-making process and the factors that influence the predictions. Overall, while ML can be a powerful tool for diabetes prediction, it is important to consider these limitations and use appropriate techniques to address them. Using machine learning (ML) in diabetes prediction in health monitoring can make significant contributions to society by improving early detection and intervention of the disease, reducing healthcare costs, and improving the overall health outcomes for individuals with diabetes. ML-based models can assist healthcare providers in identifying individuals at risk for diabetes and in monitoring the progression of the disease, allowing for more efficient and cost-effective management. Additionally, ML can also help to reduce health disparities by identifying high-risk populations that may not have access to traditional screening methods. For technology transfer, researchers and industry partners need to work together to develop and test the models in real-world settings, identify the challenges and limitations of the models, and adapt the models to meet the specific needs of the target population. Furthermore, regulatory bodies, such as FDA, should be involved to ensure the safety and efficacy of the developed products. As specialists in the field guarantee, the prediction life cycle is based on the amount of data used, and as mentioned earlier, this research could be a part of a huge project that helps in managing the healthcare sector based on AI. It could be efficient for a long time when more data are collected using the proposed system.