https://github.com/msikorski93/detecting-panic-disorder

Panic disorder detecting using machine learning techniques.
https://github.com/msikorski93/detecting-panic-disorder

artificial-neural-networks classification knn logistic-regression machine-learning panic-disorder random-forest scikit-learn sgd svm tensorflow xgboost

Last synced: 8 months ago
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Panic disorder detecting using machine learning techniques.

• Host: GitHub
• URL: https://github.com/msikorski93/detecting-panic-disorder
• Owner: msikorski93
• Created: 2023-06-16T15:57:31.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-06-21T11:00:47.000Z (over 2 years ago)
• Last Synced: 2025-01-09T07:51:11.549Z (9 months ago)
• Topics: artificial-neural-networks, classification, knn, logistic-regression, machine-learning, panic-disorder, random-forest, scikit-learn, sgd, svm, tensorflow, xgboost
• Language: Jupyter Notebook
• Homepage:
• Size: 71.3 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Detecting-Panic-Disorder

![ alt text ](https://img.shields.io/badge/license-MIT-green?style=&logo=)

![ alt text ](https://img.shields.io/badge/-Jupyter-F37626?logo=Jupyter&logoColor=white)

![ alt text ](https://img.shields.io/badge/-scikit--learn-F7931E?logo=scikitlearn&logoColor=white)

![ alt text ](https://img.shields.io/badge/-pandas-150458?logo=Pandas&logoColor=white)

![ alt text ](https://img.shields.io/badge/-TensorFlow-FF6F00?logo=TensorFlow&logoColor=white)

![ alt text ](https://img.shields.io/badge/-Keras-D00000?logo=Keras&logoColor=white)

The main motive behind this notebook was to develop accurate models or frameworks for detecting panic disorder. Based on collected symptoms and demographic data of respondents a binary classification was performed using machine learning techniques. To complete this task, we developed seven diverse classification models and evaluated their performance (displayed in table). We proved that class predicting in this dataset is possible. The best solution for this problem was applying the extreme gradient boosting model.

|           | SVM      | RF       | LR       | XGB      | KNN      | SGD      | ANN      |

|-----------|----------|----------|----------|----------|----------|----------|----------|

| Accuracy  | 0.817111 | 0.965667 | 0.960694 | 0.967833 | 0.959028 | 0.843556 | 0.965139 |

| F1-score  | 0.303575 | 0.476271 | 0.242100 | 0.599030 | 0.288471 | 0.325670 | 0.476865 |

| Precision | 0.180186 | 0.645977 | 0.599469 | 0.618741 | 0.512864 | 0.198193 | 0.629263 |

| Recall    | 0.963087 | 0.377181 | 0.151678 | 0.580537 | 0.200671 | 0.912752 | 0.383893 |

| AUC       | 0.886948 | 0.684128 | 0.573651 | 0.782546 | 0.596221 | 0.876660 | 0.687064 |

| TN        | 27981    | 34202    | 34359    | 33977    | 34226    | 29008    | 34173    |

| FP        | 6529     | 308      | 151      | 533      | 284      | 5502     | 337      |

| FN        | 55       | 928      | 1264     | 625      | 1191     | 130      | 918      |

| TP        | 1435     | 562      | 226      | 865      | 299      | 1360     | 572      |

| MCC       | 0.371385 | 0.477658 | 0.288230 | 0.582614 | 0.303686 | 0.382023 | 0.475013 |