# Machine Learning-Based Approach for Predicting Diabetes Employing Socio-Demographic Characteristics
## 3 Result Analysis and Discussion
### Discussion  
Researchers have conducted a significant amount of research on diabetes prediction, but there is still room for improvement in diabetes prediction research. For predicting diabetes in this work, we employ a socio-demographic diabetes dataset. After collecting the dataset, we pre-processed it to make it suitable for further analysis. We applied six supervised ML algorithms, namely DT, RF, SVM, XGBOOST, LGBM, and MLP to predict diabetes. After applying the ML approaches, we assessed the results of the applied ML approaches utilizing different performance metrics like accuracy, precision, recall, f1-measure, sensitivity, specificity, kappa statistics, and MCC. Among the applied ML algorithms, RF shows the highest result with a 99.37% accuracy; 1.00 precision; 0.9902 recall; 0.9951 f1-score; 1.00 sensitivity; 0.9902 specificity; as well as kappa statistics and MCC of 0.9859 and 0.9860, respectively, for the train-test split technique, which effectively predicts diabetes. The same socio-demographic diabetes dataset that we analyzed for diabetes prediction was also analyzed by [PERSON], [PERSON] et al. (2020) and has been shown to have the highest result of 99.00% accuracy for the RF approaches [6]. And [PERSON], [PERSON] et al. (2022) also used the same dataset and obtained a 94.87% accuracy; 0.9552 sensitivity; 0.9438 specificity; and 0.9412 f1-score [11]. The impact of features on the model plays an essential role in the ML field for any disease prediction. Therefore, in this work, we also show the features' impact on model prediction of the six ML algorithms by utilizing the SHAP summary plot, which is graphically expressed in Figure 7.  
According to Table 6, it has been found that our proposed model is highly capable of predicting diabetes using only socio-demographic features. In addition to that, the proposed model is validated by more evaluation metrics than the existing models. The proposed model has some practical application in different fields such as early diabetes risk assessment, customized diabetes prevention plans, community health initiatives, telehealth and remote monitoring, and others. In addition to that, this study will contribute to develop  
Figure 7: SHAP summary plot for features’ impact on model prediction. (**A**) Features’ importance on model prediction by DT, (**B**) Features’ importance on model prediction by RF, (**C**) Features’ importance on model prediction by SVM, (**D**) Features’ importance on model prediction by XGBOOST, (**E**) Features’ importance on model prediction by LGBM, (**F**) Features’ importance on model prediction by MLP.  
personalized diabetes management apps and personalized medicine. In brief, this study demonstrates many potential real-life applications in the health sector.  
However, this study has some limitations. First of all, the number of instances in this dataset is only 520, which is enough to build an ML-based prediction model but not good enough. As a result, we should collect more data in the future. The attributes of the diabetes dataset are only socio-demographic, but the socio-demographic data on diabetes are not sufficient to accurately predict diabetes. For that reason, we should collect clinical data in the future and merge them together to build an effective diabetes prediction model. Also, in the future, this study should be focused on utilizing more effective ML approaches to build an effective prediction model and develop an end-user website for diabetes prediction. Alongside its weakness, some of the strengths of this study include proposing a low-cost and efficient machine learning model with high accuracy. The model has shown a significant outcome in terms of predicting diabetes in early stages.