# An Optimized Neuro_Fuzzy Based Regression Trees for Disease Prediction Framework
## 4 Proposed Methodology
### Ant Lion Optimizer
Then, the system asks the user to enter the sugar value. If the value exceeds the normal range, the patient has diabetes. Finally, the system asks to enter the body temperature. The patient's temperature is within the normal range and displays the average temperature. When illnesses are present, sensitivity is a classifier's chance of predicting the positive. This is also known as the actual positive rate, and the formula for calculating it is presented in Equation (21). Figure 13 displays the sensitivity vs. specification for predicting the disease using generalized approximate reasoning base intelligence control (GARIC) with regression rules. Figure 10.5

When the specificity is high, the classifier has a reasonable probability of predicting the disease. The disease does not emerge in our bodies when the specificity is low. The true negative rate is another name, and it may be determined using Equation (22). Figure 10.6Figure 14 compares sensitivity-specificity with Hs-CT I and Hybrid-DT. Table 1 illustrates the analysis of the false positive rate of different techniques. One of the most important metrics used to evaluate a classifier's performance is accuracy. It's calculated as a percentage of correctly identified samples, and the formula is presented in Equation (23). Figure 15 compares accuracy with existing techniques. Table 2 illustrates the accuracy and precision analysis. Figure 10.7

When a test is positive, the positive predicted value is the chance of a classifier sending illnesses. It is also known as precision and is displayed in Equation (24). Figure 16 shows the comparison of precision with existing techniques. Figure 10.8

When the test is negative, the negative predictive value represents the chance of the illness being sent by the classifier. To validate the performance of the designed model, a few more methods are also considered. The techniques include PCA, FETCH, ANN, DPSS, Epistemological Framework (EF) [32], and Epistemological and Bibliometric Analysis (EBA) [32]. Moreover, the accuracy and precision comparison is described in Figure 10.9

## 6 Conclusions

A novel optimized fuzzy based prediction framework was developed in this article to predict the heart diseases. This method is a hybrid incorporating the Generalized approximate Reasoning base Intelligence Control (GARIC) and Ant Lion Optimization (ALO) approach. This neuro-fuzzy based approach helps in identifying the heart diseases accurately based on the sensed disease symptom data. Moreover, the integration of fuzzy logic in the proposed model predicts the disease type and measures its severity level. In addition, the incorporation of ant lion fitness function improves the prediction rate. The designed model was evaluated with IoT-based medical dataset containing sensed disease features was considered. The presented was implemented in MATLAB system and the results are estimated as accuracy, precision, and specificity. Using our proposed methodology, we obtained a prediction rate of 97% and an accuracy of 99.86%. The Internet of Things (IoT) links service providers and hospitals. As a result, if people are preoccupied with certain diseases, they will make a decision. Moreover, a comparative analysis was performed to verify that the designed model attained better results than other existing approaches. In addition, the improvement score was estimated for the presented approach. The comparative analysis shows that in the proposed model, the accuracy is improved by 7.25%, and the precision value is enhanced by 6.05%. Thus, the system accurately classifies the disease type and determines the severity rate. However, the designed model is not applicable for any other applications. Moreover, the resource usage and time consumption are high in the proposed method. So, in future, designing a multimodal deep learning with optimized features will provide the finest results. Hence, the resource usage and time consumption can be minimized. Conceptualization, A.V. and G.A. ; methodology, A.V. ; software, A.K.G. ; validation, A.V., G.A. and A.K.G.