Sociodemographic Variables in Offender and Non-Offender Patients Diagnosed with Schizophrenia Spectrum Disorders—An Explorative Analysis Using Machine Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Population
2.1.1. Forensic Psychiatric Subpopulation (OP)
2.1.2. General Psychiatric Subpopulation (NOP)
2.2. Data Source and Extraction
2.3. Selection of Predictor Variables
2.4. Data Analysis Using Machine Learning
2.4.1. Preprocessing
2.4.2. Training of the Algorithm
2.4.3. Validation of the Algorithm
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Variable Code | Variable Label |
y (Outcome variable) | Is the patient forensic? |
SD1 | Age at admission? |
SD2 | Sex according to patient files |
SD3a | Country of birth: Switzerland? |
SD3b | Country of birth: Balkan region country? |
SD3c | Country of birth: other European country? |
SD3d | Country of birth: Middle east? |
SD3e | Country of birth: Africa? |
SD3f | Country of birth: other country? |
SD3g | If 3b–3f do not apply, legal residence in Switzerland? |
SD4a | Christian faith? |
SD4b | Islamic faith? |
SD5a | Marital Status (at time of the investigated offence)—married? |
SD5b | Marital Status (at time of the investigated offence)—single? |
SD6a | Living situation (at time of the investigated offence)—mental health care institution |
SD6b | Living situation (at time of the investigated offence)—assisted living |
SD6c | Living situation (at time of the investigated offence)—home alone |
SD6d | Living situation (at time of the investigated offence)—home with others |
SD6e | Living situation (at time of the investigated offence)—at parents’ home |
SD6f | Living situation (at time of the investigated offence)—with relatives |
SD6g | Living situation (at time of the investigated offence)—homeless |
SD6h | Living situation (at time of the investigated offence)—prison |
SD6i | Living situation (at time of the investigated offence)—other |
SD7a | Highest graduation (at time of the investigated offence)—no compulsory schooling |
SD7b | Highest graduation: (at time of the investigated offence)—compulsory schooling |
SD7c | Highest graduation (at time of the investigated offence)—graduation |
SD7d | Highest graduation (at time of the investigated offence)—college/university |
SD8a | Learned profession: no apprenticeship |
SD8b | Learned profession: college/university degree |
SD8c | Learned profession: official/civil servant |
SD8d | Learned profession: mercantile job |
SD8e | Learned profession: non-mercantile job |
SD8f | Learned profession: crafting job |
SD8g | Learned profession: other job |
SD9 | Is the patient a nonworker (at time of the investigated offence)? |
SD11 | Is the patient a nonworker (majority of occupational time)? |
SD12a | Rank at job: basal (majority of occupational time)? |
SD12b | Rank at job: complex (majority of occupational time)? |
SD14 | Own children? |
SD15 | Any siblings? |
SD17 | Was the legal guardian married? |
SD18a | Who was/is the legal guardian—birth parents? |
SD18b | Who was/is the legal guardian—single parents? |
SD18c | Who was/is the legal guardian—step-parents? |
SD18d | Who was/is the legal guardian—one step-parent? |
SD18f | Who was/is the legal guardian—grandparents? |
SD18g | Who was/is the legal guardian—foster parents? |
SD18h | Who was/is the legal guardian—child home? |
SD19 | Member in a (leisure) club? |
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Performance Measures | Explanation |
---|---|
Receiver operating characteristics, area under the curve (AUC) | Overall ability of a model to discriminate between two groups, as indicated by graph plotting sensitivity and 1-specificity. The higher the AUC, the better the model distinguishes between positive and negative classes. |
Balanced Accuracy | The average of sensitivity and specificity, providing a single measure that allows for interpreting both false positives and false negatives. |
Sensitivity | The ability of a model to correctly identify true positives. Sensitivity is also called recall or the true positive rate. |
Specificity | The ability of a model to correctly identify true negatives, also called the true negative rate. |
Positive predictive value (PPV) | The proportion of positive test results that are true positives. Used to interpret an individual’s actual probability of being a true positive in case of a positive test result. |
Negative predictive value (NPV) | The proportion of negative test results that are true negatives. Used to interpret an individual’s actual probability of being a true negative in case of a negative test result. |
Variable Description | OP n/N (%) | Mean (SD) | NOP n/N (%) | Mean (SD) |
---|---|---|---|---|
Age at admission | 34.2 (10.2) | 36.2 (12.2) | ||
Sex *: male | 339/370 (91.6) | 339/370 (91.6) | ||
Country of birth: Switzerland | 167/370 (45.1) | 245/367 (66.8) | ||
Marital status: Single | 297/364 (81.6) | 282/364 (77.5) | ||
Diagnosis: Schizophrenia | 294/370 (79.5) | 287/370 (77.6) | ||
Co-Diagnosis: Addiction Disorder | 269/200 (72.9) | 183/327 (56) | ||
Co-Diagnosis: Personality Disorder | 47/370 (12.7) | 26/370 (7) |
Statistical Procedure | Balanced Accuracy (%) | AUC | Sensitivity (%) | Specificity (%) | PPV (%) | NPV (%) |
---|---|---|---|---|---|---|
Logistic Regression | 62.20 | 0.68 | 74.90 | 49.50 | 58.20 | 69.60 |
Tree | 63.00 | 0.64 | 82.30 | 43.80 | 57.70 | 74.00 |
Random Forest | 62.4 | 0.68 | 78.3 | 46.5 | 57.9 | 73.4 |
Gradient Boosting | 63.3 | 0.69 | 76.7 | 49.9 | 59.3 | 72.1 |
KNN | 56.9 | 0.59 | 78.9 | 35 | 44.6 | 85.9 |
SVM | 61.9 | 0.68 | 73.8 | 49.9 | 58.1 | 68.9 |
Naive Bayes | 62.6 | 0.68 | 62.3 | 62.9 | 61.2 | 65.3 |
Variable Description | OP n/N (%) | NOP n/N (%) |
---|---|---|
Country of birth: Switzerland | 167/370 (45.1) | 245/367 (66.8) |
Illegal residence in Switzerland | 95/370 (25.7) | 34/367 (9.3) |
Graduation: did not complete compulsory schooling | 89/342 (26) | 18/321 (5.6) |
Performance Measures | % (95% CI) |
---|---|
AUC | 0.65 (0.58–0.72) |
Balanced Accuracy | 63.1 (56.5–69.1) |
Sensitivity | 63 (53.6–71.6) |
Specificity | 63.1 (53–72.2) |
PPV | 66.4 (56.8–74.8) |
NPV | 59.6 (49.8–68.8) |
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Hofmann, A.B.; Dörner, M.; Machetanz, L.; Kirchebner, J. Sociodemographic Variables in Offender and Non-Offender Patients Diagnosed with Schizophrenia Spectrum Disorders—An Explorative Analysis Using Machine Learning. Healthcare 2024, 12, 1699. https://doi.org/10.3390/healthcare12171699
Hofmann AB, Dörner M, Machetanz L, Kirchebner J. Sociodemographic Variables in Offender and Non-Offender Patients Diagnosed with Schizophrenia Spectrum Disorders—An Explorative Analysis Using Machine Learning. Healthcare. 2024; 12(17):1699. https://doi.org/10.3390/healthcare12171699
Chicago/Turabian StyleHofmann, Andreas B., Marc Dörner, Lena Machetanz, and Johannes Kirchebner. 2024. "Sociodemographic Variables in Offender and Non-Offender Patients Diagnosed with Schizophrenia Spectrum Disorders—An Explorative Analysis Using Machine Learning" Healthcare 12, no. 17: 1699. https://doi.org/10.3390/healthcare12171699