# An Optimized Neuro_Fuzzy Based Regression Trees for Disease Prediction Framework
## 4 Proposed Methodology
### Ant Lion Optimizer
In the designed model, the normal, high, and low level of the features are set in three states, where the normal rate of the body features is indicated as \"0\", the high level is denoted as \"1\", and low level is indicated as \"\(-\)1\". The proposed model is a hybrid technique which is the combination of advanced neuro-fuzzy with an ALO algorithm. This model has five different layers, i.e., the input and output fuzzification layer and three hidden layers. Subsequently, the filtered input data is given to n number of patients in the GARLIC-ALO input layer. Moreover, features, such as respiratory rate, HDL cholesterol, age, heartbeat rate, gender, body temperature, systolic and diastolic blood pressure, LDL, and total cholesterol of the patient, are considered as the input data. These features are trained to the input layer, and the fuzzification output layer is given to the second layer. The predicted result calculates the variation between the actual output and error rate. ## 5 Result and Discussion

The approaches we offer are implemented in MATLAB version 2018 on a Windows 7 environment. Math Works designed a numerical and limiting indotctrination language. We use the data mining learning approach in this research and do tests with the dataset. GARLIC using regression rules provides the maximum accuracy, according to our findings. Therefore, it increases the performance of cardiac illnesses prognosis. Assess the categorization method's accuracy, specificity, and sensitivity. Because the sensitivity and specificity are both high, the accuracy is as well. When illnesses are present, sensitivity is used to forecast the positive. This is also known as the true positive rate (TPR). When the illness is not present, specificity is used to forecast the negative. It is also called true negative rate (TNR). Figure 6 displays the execution results: Intelligent heart disease partition. Figures 7 and 8 show the execution results: patients not shown in Figure 8a; registration completed shown in Figure 8b; patient login shown in Figure 8c. Neural network training state shown in Figure 9. Iteration vs. best score obtained so far for predicting the severity using Ant Lion Optimization shown in Figure 10. Figure 11 shows the result: Menu button Figure 11a and Sugar check-up shown in Figure 11b. Final report for patient shown in Figure 12. Sensitivity vs. specification for predicting the disease using GARIC with regression rules is shown in Figure 13.

Figure 8: Execution results: (a) Patient no (b) Registration completed (c) Patient login. Figure 10: Iteration vs. best score obtained so far for predicting the severity using Ant Lion Optimization algorithm. Figure 9: Neural network training state. * Report
* Patients name : [PERSON]
* Patients age : 45
* Sex : [PERSON]
* Enter the systolic pressure:140
* Enter the diastolic pressure:120
* Enter the value of LDL cholesterol:1239
* Enter the value of HDL cholesterol:200
* total_cholesterol =
* 479
* Risk of Heart disease,high cholesterol is linked to diabetes and high BP
* LDL cholesterol High
* HDL cholesterol High
* Enter the value of triglycerides cholesterol:180
* Low triglycerides cholesterol
* Enter the value of Heart beat: 98
* Normal Heart beat
* Enter the value of sugar:130
* Low sugar
* Enter the value of body temperature: 98
* Normal temperature

Figure 11: Result: (a) Menu Button (b) Sugar Check Up. Figure 12: Final report for patient. Figure 13: Sensitivity vs. specification for predicting the disease using Generalize approximate Reasoning base Intelligence Control (GARIC) with regression rules. ### Performance

The performance is discussed on behalf of the confusion matrix and its components. Comparison of sensitivity (false positive rate) and specificity (true positive rate) with Hs-CT I (high sensitivity cardiac troponin I and Hybrid-DT) is shown in Figure 10.0 A comparison of accuracy with existing techniques.