Content uploaded by Milad Payandeh
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All content in this area was uploaded by Milad Payandeh on Aug 31, 2023
Content may be subject to copyright.
m.payandeh@qodsiau.ac.ir
Principal Component Analysis – PCA
R2 Score
[1]
[4]
BIA
[1]
Apriori Matlab
[2]
Levenberg-MarquardtBayesian Regularization
Bayesian Regularization
Levenberg-Marquardt
L.MB.R
[3]
[4]
῀ SVM
[5]
SKF
]6[
New System Solver
Bayes
IVNBBMI
IV
[7]
Bias Error Control Term
Wilcoxon
[8]
VIKOR (VMFET)
[9]
Y
X
Wr
Foar
Bice
Ank
Kn
Th
Hi
Abd
Ch
Ne
He
We
Ag
BDP
D
18.2
28.6
32.2
23.1
38.5
59.4
99.9
92.5
100.8
37.9
70.1
178.9
44.8
19.15
1.05
mean
0.93
2.02
3.02
1.69
2.41
5.24
7.16
10.78
8.43
2.43
3.66
29.38
12.60
8.36
0.01
std
15.8
21
24.80
19.1
33.00
47.20
85
69.40
79.30
31.10
29.50
118.5
22.00
00.00
0.99
min
17.6
27.3
30.20
22
36.97
56.00
95.5
84.57
94.35
36.40
68.25
159
35.75
12.47
1.04
%25
18.3
28.7
32.05
22.8
38.50
59.00
99.3
90.95
99.65
38
70
176
43.00
19.20
1.05
%50
18.8
30
34.32
24
39.92
62.35
103.5
99.32
105.3
39.42
72.25
197
54.00
25.30
1.07
%75
21.4
34.9
45.00
33.9
49.10
8730
147.7
148.1
136.2
51.20
77.75
363.1
81.00
47.50
1.10
max
0=
min
BDP
kg/m³lb
inchcm
Heatmap
[-1,+1]
MinMaxScaler
[0,1]
YX
Feature
Sample
Dataset
14
252
X.shape
1
252
Y.shape
Random_State = 0
Feature
Sample
Dataset
8
176
Train.X
8
76
Test.X
[-1,+1]
PCA
Fit
0.969162Score =
2
R0.979228Score =
2
R
Relu
Score
2
Test R
Score
2
Train R
0.913840
0.899473
NN
0.979228
0.969162
Proposed Method
Regulize
[10]
CSV
[11]
Python
NumpyPandasScipySeabornScikit-learnMatplotlib
¬
1.
2.
3. Levenberg-
MarquardtBayesian Regularization
4.
5.
svm
6. Zamri, N.B.A., Bhuvaneswari, T., Aziz, N.A.B.A. and Aziz, N.H.B.A., 2018, July. Feature selection using
simulated Kalman filter (SKF) for prediction of body fat percentage. In Proceedings of the 2018 1st
International Conference on Mathematics and Statistics (pp. 23-27).
7. Mazalan, N.M., 2017. Prediction of body fat status by using naïve bayes technique among university students
(Doctoral dissertation, Universiti Teknologi MARA).
8. Chiong, R., Fan, Z., Hu, Z. and Chiong, F., 2021. Using an improved relative error support vector machine
for body fat prediction. Computer Methods and Programs in Biomedicine, 198, p.105749.
9. Lai, C.M., Chiu, C.C., Shih, Y.C. and Huang, H.P., 2022. A hybrid feature selection algorithm using simplified
swarm optimization for body fat prediction. Computer Methods and Programs in Biomedicine, 226, p.107183.
10. https://www.kaggle.com/datasets/fedesoriano/body-fat-prediction-dataset
11. https://github.com/milad71payandeh/Body-Fat-Prediction
1 Body Mass Index (BMI)
2 Body Fat Percentage
3 Machine Learning
4 Deep Learning
5 Dataset
6 Time Complexity
7 Principal Component Analysis (PCA)
8 Dimension Reduction
9 Linear Regression
10 Data Mining
11 Data Recovery
12 Big Data
13 Database Management
14 Multi Layer Perceptron (MLP)
15 Apriori
16 Support Vector Machine (SVM)
17 Simulated Kalman Filter (SKF)
18 Particle Swarm Optimization (PSO)
19 Metaheuristic
20 Naïve Bayes
21 Density
22 Label
23 Range
24 Train Set
25 Test Set
26 Correlation Rate
27 Pearson Correlation Coefficient
28 R2 Score