# Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol

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^{2}

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## Abstract

**:**

## 1. Introduction

#### 1.1. Background

#### 1.2. Application of Statistical Models in Crash Severity Prediction

#### 1.3. Application of Machine Learning Models in Crash Severity Prediction

#### 1.4. Artificial Neural Networks

#### 1.5. Support Vector Machine

#### 1.6. Fuzzy C-Means Clustering

_{ij}is the membership value of x

_{j}for the cluster I; x

_{j}is the j

^{th}of d-dimensional measured data; c

_{i}is the d-dimension center of the cluster; and ||*|| is the Euclidean distance between any training vector and the center.

#### 1.7. Study Objectives

#### 1.8. Outline

## 2. Data Set Description

- The injury that causes a person to be detained in hospital as an in-patient for an extended period and which may have required surgery.
- An injury that will have lasting or even permanent implications for the injured person and that will have an impact upon their ability to work or which involve a change to their level of independence.
- An injury that causes death 30 or more days after the accident.

## 3. Model Development

#### 3.1. Feedforward Neural Networks

#### 3.2. Support Vector Machine

#### 3.3. FCM-Based FNN and SVM

## 4. Results and Discussion

## 5. Conclusions

#### Limitations and Future Study

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 5.**Confusion matrices for feed-forward neural network (FNN) model (training and testing data).

**Figure 7.**Confusion matrices for FNN combined with fuzzy c-means (FCM) clustering (training and testing data).

Input Variables | Data Type | No. of Categories |
---|---|---|

Vehicle attributes | ||

Number of vehicles involved | Numeric | - |

Vehicle type | Nominal | 12 |

Road condition attributes | ||

Road type | Nominal | 5 |

Junction type | Nominal | 9 |

Junction control | Nominal | 5 |

Light | Nominal | 5 |

Weather | Nominal | 9 |

Road surface condition | Nominal | 7 |

Area type | Nominal | 2 |

Speed limit | Numeric | - |

Road class | Nominal | 6 |

Crash attributes | ||

Number of causalities | Numeric | - |

Day of the week | Numeric | 7 |

No. of Clusters | FNN-FCM^{1} Testing Accuracy (%) | SVM-FCM^{2} Testing Accuracy (%) |
---|---|---|

1 | 70.0 | 73.0 |

2 | 71.8 | 72.2 |

3 | 71.0 | 73.0 |

4 | 70.2 | 74.2 |

5 | 67.9 | 72.1 |

^{1}FNN-FCM: fuzzy c-means clustering based feed-forward neural network.

^{2}SVM-FCM: fuzzy c-means clustering based support vector machine.

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**MDPI and ACS Style**

Assi, K.; Rahman, S.M.; Mansoor, U.; Ratrout, N.
Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol. *Int. J. Environ. Res. Public Health* **2020**, *17*, 5497.
https://doi.org/10.3390/ijerph17155497

**AMA Style**

Assi K, Rahman SM, Mansoor U, Ratrout N.
Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol. *International Journal of Environmental Research and Public Health*. 2020; 17(15):5497.
https://doi.org/10.3390/ijerph17155497

**Chicago/Turabian Style**

Assi, Khaled, Syed Masiur Rahman, Umer Mansoor, and Nedal Ratrout.
2020. "Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol" *International Journal of Environmental Research and Public Health* 17, no. 15: 5497.
https://doi.org/10.3390/ijerph17155497