Application of the Certainty Factor Method for Diagnosing Mental Illness Disease
Bibliographic record
Abstract
Mental illness is a disease that is widespread among Indonesian people. Mental illness, also known as mental health disorder, is a term that refers to various conditions that can affect a person's thoughts, moods, feelings or behavior. However, there are still many Indonesian people who do not recognize and indicate the existence of mental illness because many people do not pay attention to their mental health or those around them. the small number of psychiatrists available in each area and the costs required are also not small, causing ordinary people to be reluctant to carry out examinations with psychiatrists, this of course leads to delays in treatment which can even be fatal. To prevent the increase in sufferers of mental illness, a system is needed that can store the knowledge of experts or psychologists who understand how to handle mental illness. An expert expert system is an artificial intelligence program that combines a knowledge base with an inference system to emulate an expert. The certainty factor method is a method used to solve cases of uncertainty, where the size is based on a fact or rule that can be used in expert systems. With the existence of an expert system for diagnosing mental illness, the general public can recognize early symptoms of mental illness, so treatment can be done earlier. From the results of the trials conducted, the results of the mental illness expert system were obtained with the highest score, namely depression with a confidence value of 90.02%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".