Understanding the data

Analysis of psychometric data

Example of final product: analysis of psychometric data

Start from computerized test or scanning the paper forms.
MMPI test has 550 questions; any similar test may be computerized.

Store results in a database for future reference

Compute coefficients (scales) measuring different tendencies.
MMPI scales 1-4 used for control, next 10 coefficients are clinical scales: hypochondria, depression, hysteria, psychopathy, paranoia, schizophrenia etc.

Display scales in a “psychogram”, interpreted by skilled psychologists diagnosing specific problems; show rules that are true for this case. Rules are derived from data collected in the Academic Psychological Clinic of Nicolaus Copernicus University and in several psychiatric hospitals around Poland.

Two datasets used, woman and man, over 1600 cases each, 27 classes (normal, neurotic, drug addicts, schizophrenic, psychopaths, organic problems, malingerers, persons with criminal tendencies etc.).

2-3 rules per class found, a total of 50-100 rules.

Analyze how each rule fits to the case; vary uncertainty of input measurement (optimal uncertainty has been calculated by minimization of generalization error).

Show probabilities of different diagnoses, graph their dependence on the uncertainity of inputs.

Show verbal interpretation of cases and rules.

If probability of new classes quickly grows with the assumed uncertainty of the measurement analyze probabilistic confidence levels.

Multidimensional scaling (MDS) allows to see the case in relation to known cases.

Probabilities of different diagnoses may be interpolated to show change of the mental health over time.
Probabilistic confidence levels allow to see detailed changes (show movie here).

Rules are very important here, allowing for detailed interpretation.

Rules generated using SSV classification tree and FSM neural network.

System	Data	# rules	Accuracy	Fuzzy
C4.5	Women	55	93.0%	93.7%
	Men	61	92.5%	93.1%
FSM	Women	69	95.4%	97.6%
	Men	98	95.9%	96.9%

10-fold crossvalidation gives 82-85% correct answers with FSM (crisp unoptimized rules), and 79-84% correct answers with C4.5. Fuzzification improves FSM crossvalidation results to 90-92%.

Some questions:
How good are our experts?
How to measure the correctness of such system?
Can we provide useful information if diagnosis is not reliable?
How to deal with several disease - automatic creation of new classes?

Włodzisław Duch

Compute coefficients (scales) measuring different tendencies. MMPI scales 1-4 used for control, next 10 coefficients are clinical scales: hypochondria, depression, hysteria, psychopathy, paranoia, schizophrenia etc.
Display scales in a “psychogram”, interpreted by skilled psychologists diagnosing specific problems; show rules that are true for this case. Rules are derived from data collected in the Academic Psychological Clinic of Nicolaus Copernicus University and in several psychiatric hospitals around Poland.

Analyze how each rule fits to the case; vary uncertainty of input measurement (optimal uncertainty has been calculated by minimization of generalization error).
Show probabilities of different diagnoses, graph their dependence on the uncertainity of inputs.
Show verbal interpretation of cases and rules.
If probability of new classes quickly grows with the assumed uncertainty of the measurement analyze probabilistic confidence levels.