Algorithm 1.

Showcasing the Major Working Steps of RDKVT.

1: Inputs: Dataset, $D={\sum }_{i=1}^M\left(X_i,Y_i\right)$

2: Outputs: Classify whether the thyroid is affected or not

3: D(a) ← D.drop ([T BG], axis = columns)

4: D(b) ← MedianImpute {$D^{\left(a\right)}$}

5: D(c) ← LabelEncoder {$D^{\left(b\right)}$}

6: D(d) ← SMOTEENN {$D^{\left(c\right)}$}

7: $X_i,Y_i$ ← input {$D^{\left(d\right)}$(N × M matrix)}, output (N × 1 vector)

8: ${Xtr}_7,{Ytr}_7,{Xte}_3,{Yte}_3$ ← TrainTestSplit ($X_i,Y_i,0.3$)

9: ${Xtr}_8,{Ytr}_8,{Xte}_2,{Yte}_2$ ← TrainTestSplit ($X_i,Y_i,0.2$)

10: ${Xtr}_9,{Ytr}_9,{Xte}_1,{Yte}_1$ ← TrainTestSplit ($X_i,Y_i,0.1$)

11: while (execute − different − TrainTestSets) do

12: ${BC}^{\left(1\right)}$ ← RandomForest (${Xtr}_i,{Ytr}_i,{Yte}_i$)

13: ${BC}^{\left(2\right)}$ ← DecisionTree (${Xtr}_i,{Ytr}_i,{Yte}_i$)

14: ${BC}^{\left(3\right)}$ ← KNearestNeighbors (${Xtr}_i,{Ytr}_i,{Yte}_i$)

15: end while

16: procedure RDKVT (${Xtr}_i,{Ytr}_i,{Yte}_i$)

17: RDKVT ← argmax (${BC}^1,{BC}^2,and{BC}^3$)

18: while (fitting – different − TrainTestSets)

19: RDKVT ← RDKVT.fit (${Xtr}_i,{Ytr}_i$)

20: end while

21: Result ← RDKVT. predict (New−sample)

22: ReturnResult