# An Optimized Neuro_Fuzzy Based Regression Trees for Disease Prediction Framework
## 4 Proposed Methodology
### Ant Lion Optimizer  
Ant Lion Optimization (ALO) is a nature inspired meta-heuristic algorithm developed for optimization problems. It is based on the hunting behavior of antlion. It digs a hole in form of cone shape using its jaws and then it hides itself in the bottom region of the cone. When an ant traps inside the cone, it starts to throw sand towards the trap in order to bury the prey. Moreover, it has numerous advantages, such as it is easy to implement, scalable, flexible, and demonstrates a great balance between exploration and exploitation. Here, the ALO approach is applied to measure the severity of the disease. ALO has five essential steps on hunts: random continues about ants, constructing traps, etc. The random selection of test cases is described in Equation (17). The ALO algorithm presents the mathematical models, which, in general, follow the algorithms shown in Equation (17)  
\[\text{S (t) }= \begin{cases}1 rand>0.5\\ 0 sand\leq 0.5\end{cases}  \tag{17}\]  
A maximum number over a generation is m. S (t) is a stochastic feature, then rand is a wide variety generated, including equal allocation among the end of [0, 1]. The location concerning ants stays stored between the following matrixes shown within Equation (18)  
\[N_{ANT}= \begin{bmatrix}B_{1,1}&B_{1,2}&.&B_{1,d}\\ B_{2,1}&B_{2,2}&.&B_{2,d}\\.&.&.&.\\ B_{n,1}&B_{n,2}&.&B_{n,d}\end{bmatrix}  \tag{18}\]  
where \(N_{ant}\) is the saving the position concerning every ant, \(B\) the value regarding the \(ith\) variable about stability \(kth\). \(M\) is the number regarding ants or range on the variable is \(d\)The equivalent objective value is selected as every ant lion is calculated or stored among the similar form shown in Equation (19)  
\[N_{oA}= \begin{bmatrix}g[B_{1,1}&B_{1,2}&.&B_{1,d}]\\ g[B_{2,1}&B_{2,2}&.&B_{2,d}]\\.&.&.&.\\ g[B_{n,1}&B_{n,2}&.&B_{n,d}]\end{bmatrix}  \tag{19}\]  
where \(N_{OA}\) is the saving the position concerning each ant? \(B_{j,k}\) is the value regarding the durability \(ith\) variable on longevity \(kth\). M is the number concerning ants, the range on the variable is \(d\), then \(g\) is the objective function. The central equation is shown in Equation (20). The flow of the proposed model is illustrated in the form of pseudo code in Algorithm 1. \[N_{oA}= \begin{bmatrix}g[BM_{1,1}&BM_{1,2}&.&BM_{1,d}]\\ g[BM_{2,1}&BM_{2,2}&.&BM_{2,d}]\\.&.&.&.\\ g[BM_{n,1}&BM_{n,2}&.&BM_{n,d}]\end{bmatrix}  \tag{20}\]  
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1 Start0
2 {
3 Input
4 sense input data \(\rightarrow\) dataset
5 Fuzzy function0
6 {
7 remove \(\rightarrow\) noise (data)
8 //removing unwanted noise from the bio-signal
9 }
10 Feature exaction0
11 {
12 extract \(\rightarrow\) feature (data)
13 //exacting the feature of data
14 }
15 ALO_fitness 0
16 {
17 calculate(fitness) \(\rightarrow\) classify (diseases)
18 Heartbeat rate0
19 {
20 60-100 beats/min //normal heart beat range
21 >60-100 beats/min //high heart beat range
22 <60-100 beats/min //low heart beat range
23 }
24 Respiratory rate0
25 {
26 12-18 breaths/min, Normal;
27 <12-18 breaths/min High;
28 >12-18 breaths/min Low
29 }
30 Diastolic blood pressure0
31 {
32 60-90 mmHg, Normal;
33 >60-90 mmHg, High;
34 <60-90 mmHg, Low. Initially, the sensed medical dataset was collected from the standard site kaggle [29] and imported into the system. It contains more than 10 sensed patient samples with the labels 0 and 1. The foremost step in prediction process is pre-processing the dataset. In this phase, the noise data present in the sensed dataset is removed using filters like median, Gaussian, etc. Then, the filtered dataset enters into the feature analysis module. In this module, the disease features (features necessary for classification) are extracted. In feature analysis phase, the attributes like HDL cholesterol, body temperature, e.g., respiratory rate, systolic and diastolic blood pressure, etc., are extracted from the dataset. These extracted features are then trained using the proposed approach to classify the diseases.