### Abstract

Neural networks can be applied to many
predictive data mining applications due to their power, flexibility and
relatively easy operations. Predictive neural networks are very useful for applications where the
underlying process is complex, such as in classification using a mix of nominal and ratio level variables and for predictive validity
based on classification modelling. A neural network can approximate a wide
range of statistical models without requiring the researcher to hypothesize in
advance certain relationships between the dependent and independent variables. Neural networks
and discriminant function analysis (a more traditional statistical approach) were applied, based on a set of
dependent variables, which were biomechanical measures of isokinetic peak torque, work, power and
fatigue index for leg extension at 60 °s^{-1}, 180 °s^{-1} and 300°s^{-1}; a counter movement jump or CMJ (contact
time, flight time and peak height); and I Om acceleration were collected within a
university exercise physiology laboratory, and were compared for their classification
accuracy to successfully discriminate between physically active men and
women.
In this
research gender represented the classification variable in the statistical
analyses. Neural networks, specifically the multilayer perceptron (MLP) networks, were applied and
compared with stepwise method discriminant function analysis for classification
accuracy. Sixty participants
(male=32, age=23.9, s.d.=8.0 years; female=28,
age=21.1, s.d.=6-3 years) represented the volunteer/convenient sample in the study. The torque, work,
power and fatigue index data were normalised by dividing these scores by the participants weight to minimise weight-muscle mass
differences that are related to gender. Normalisation refers to the division of multiple sets of data by a common variable
in order to negate or diminish the variable's effect on the data, in this
context weight, thus allowing underlying characteristics of the data sets to be compared and results in the derivation of force/weight,
work/weight and power/weight ratios. The research aim focussed on which factors
or variables provided the greatest difference between the genders, the
establishment of a hierarchy of factor-variable importance and to assess which
multivariate method of classification provided the best solution. The
stepwise discriminant
method was
applied to
derive
a statistical meaningful solution in terms of
variable hierarchy of importance. The neural network classification statistics
indicated the order of importance was peak torque 180 °s^{-1}, contact time,
flight time and peak torque at 300 °s^{-}^{1} and the accuracy of classification was I 00% on the
training model but only 85% for the holdout/testing sample. Of the input variables in the stepwise
discriminant analysis four were identified as statistically significant and were in the following order of
importance, peak torque 180°s^{-1}, CMJ contact time, fatigue index at
180 °s^{-1} and total work 180°s^{-}^{1} and the percentage classification
accuracy was 90.6% males and 96.4% females ( overall 93.3%). Neural MLP networks
were more effective in predicting group membership and displayed higher
predictive validity when compared to discriminant analysis. The selected variables in both models were very
similar with peak torque 180 °s^{-1} identified as the most important
discriminating factor/variable for gender differences for normalised test
scores for torque, work, power, and CMJ and acceleration motor fitness tasks.

Original language | English |
---|---|

Title of host publication | Proceedings of the 8th International Symposium on Computer Science in Sport |

Editors | Yong Jiang, Hui Zhang |

Place of Publication | England |

Publisher | World Academic Union |

Pages | 106-109 |

Number of pages | 4 |

ISBN (Print) | 978-1-84626-087-2 |

Publication status | Published - 2011 |

Event | IACSS2011 International Association on Computer Science in Sport - Shanghai, China Duration: 21 Sep 2011 → 24 Sep 2011 |

### Conference

Conference | IACSS2011 International Association on Computer Science in Sport |
---|---|

Period | 21/09/11 → 24/09/11 |

### Fingerprint

### Cite this

*Proceedings of the 8th International Symposium on Computer Science in Sport*(pp. 106-109). England: World Academic Union.

}

*Proceedings of the 8th International Symposium on Computer Science in Sport.*World Academic Union, England, pp. 106-109, IACSS2011 International Association on Computer Science in Sport, 21/09/11.

**A Comparison of classification accuracy for gender using neural networks and discriminant funtion analysis based on biomechanical measures of isokinetic torque, work, power, fatigue index, countermovement jump and acceleration.** / Heazlewood, Ian; Boutagy, Anthony.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings › Research › peer-review

TY - GEN

T1 - A Comparison of classification accuracy for gender using neural networks and discriminant funtion analysis based on biomechanical measures of isokinetic torque, work, power, fatigue index, countermovement jump and acceleration

AU - Heazlewood, Ian

AU - Boutagy, Anthony

PY - 2011

Y1 - 2011

N2 - Neural networks can be applied to many predictive data mining applications due to their power, flexibility and relatively easy operations. Predictive neural networks are very useful for applications where the underlying process is complex, such as in classification using a mix of nominal and ratio level variables and for predictive validity based on classification modelling. A neural network can approximate a wide range of statistical models without requiring the researcher to hypothesize in advance certain relationships between the dependent and independent variables. Neural networks and discriminant function analysis (a more traditional statistical approach) were applied, based on a set of dependent variables, which were biomechanical measures of isokinetic peak torque, work, power and fatigue index for leg extension at 60 °s-1, 180 °s-1 and 300°s-1; a counter movement jump or CMJ (contact time, flight time and peak height); and I Om acceleration were collected within a university exercise physiology laboratory, and were compared for their classification accuracy to successfully discriminate between physically active men and women. In this research gender represented the classification variable in the statistical analyses. Neural networks, specifically the multilayer perceptron (MLP) networks, were applied and compared with stepwise method discriminant function analysis for classification accuracy. Sixty participants (male=32, age=23.9, s.d.=8.0 years; female=28, age=21.1, s.d.=6-3 years) represented the volunteer/convenient sample in the study. The torque, work, power and fatigue index data were normalised by dividing these scores by the participants weight to minimise weight-muscle mass differences that are related to gender. Normalisation refers to the division of multiple sets of data by a common variable in order to negate or diminish the variable's effect on the data, in this context weight, thus allowing underlying characteristics of the data sets to be compared and results in the derivation of force/weight, work/weight and power/weight ratios. The research aim focussed on which factors or variables provided the greatest difference between the genders, the establishment of a hierarchy of factor-variable importance and to assess which multivariate method of classification provided the best solution. The stepwise discriminant method was applied to derive a statistical meaningful solution in terms of variable hierarchy of importance. The neural network classification statistics indicated the order of importance was peak torque 180 °s-1, contact time, flight time and peak torque at 300 °s-1 and the accuracy of classification was I 00% on the training model but only 85% for the holdout/testing sample. Of the input variables in the stepwise discriminant analysis four were identified as statistically significant and were in the following order of importance, peak torque 180°s-1, CMJ contact time, fatigue index at 180 °s-1 and total work 180°s-1 and the percentage classification accuracy was 90.6% males and 96.4% females ( overall 93.3%). Neural MLP networks were more effective in predicting group membership and displayed higher predictive validity when compared to discriminant analysis. The selected variables in both models were very similar with peak torque 180 °s-1 identified as the most important discriminating factor/variable for gender differences for normalised test scores for torque, work, power, and CMJ and acceleration motor fitness tasks.

AB - Neural networks can be applied to many predictive data mining applications due to their power, flexibility and relatively easy operations. Predictive neural networks are very useful for applications where the underlying process is complex, such as in classification using a mix of nominal and ratio level variables and for predictive validity based on classification modelling. A neural network can approximate a wide range of statistical models without requiring the researcher to hypothesize in advance certain relationships between the dependent and independent variables. Neural networks and discriminant function analysis (a more traditional statistical approach) were applied, based on a set of dependent variables, which were biomechanical measures of isokinetic peak torque, work, power and fatigue index for leg extension at 60 °s-1, 180 °s-1 and 300°s-1; a counter movement jump or CMJ (contact time, flight time and peak height); and I Om acceleration were collected within a university exercise physiology laboratory, and were compared for their classification accuracy to successfully discriminate between physically active men and women. In this research gender represented the classification variable in the statistical analyses. Neural networks, specifically the multilayer perceptron (MLP) networks, were applied and compared with stepwise method discriminant function analysis for classification accuracy. Sixty participants (male=32, age=23.9, s.d.=8.0 years; female=28, age=21.1, s.d.=6-3 years) represented the volunteer/convenient sample in the study. The torque, work, power and fatigue index data were normalised by dividing these scores by the participants weight to minimise weight-muscle mass differences that are related to gender. Normalisation refers to the division of multiple sets of data by a common variable in order to negate or diminish the variable's effect on the data, in this context weight, thus allowing underlying characteristics of the data sets to be compared and results in the derivation of force/weight, work/weight and power/weight ratios. The research aim focussed on which factors or variables provided the greatest difference between the genders, the establishment of a hierarchy of factor-variable importance and to assess which multivariate method of classification provided the best solution. The stepwise discriminant method was applied to derive a statistical meaningful solution in terms of variable hierarchy of importance. The neural network classification statistics indicated the order of importance was peak torque 180 °s-1, contact time, flight time and peak torque at 300 °s-1 and the accuracy of classification was I 00% on the training model but only 85% for the holdout/testing sample. Of the input variables in the stepwise discriminant analysis four were identified as statistically significant and were in the following order of importance, peak torque 180°s-1, CMJ contact time, fatigue index at 180 °s-1 and total work 180°s-1 and the percentage classification accuracy was 90.6% males and 96.4% females ( overall 93.3%). Neural MLP networks were more effective in predicting group membership and displayed higher predictive validity when compared to discriminant analysis. The selected variables in both models were very similar with peak torque 180 °s-1 identified as the most important discriminating factor/variable for gender differences for normalised test scores for torque, work, power, and CMJ and acceleration motor fitness tasks.

M3 - Conference Paper published in Proceedings

SN - 978-1-84626-087-2

SP - 106

EP - 109

BT - Proceedings of the 8th International Symposium on Computer Science in Sport

A2 - Jiang, Yong

A2 - Zhang, Hui

PB - World Academic Union

CY - England

ER -