Logistic Regression Model for Depression with Treatment

Abstract

The purpose of this study was to develop a logistic regression model for depression with treatment. Depression has been the largest mental health problem affecting public health. Early detection of people with depression is crucial for effective mitigation and treatment. This can only be achieved when clear symptoms of depression are used to detect depression in patients. Despite this problem, the treatment of depression among policemen usually faces early diagnosis challenges. To achieve this, a population of 10000 was simulated and used in this study. This study developed a logistic predictive model for police officers using the symptoms exhibited by the patient. The study also incorporated the medical intervention for depression to investigate its effect on transition probabilities. The results indicated that there were reduced transition probabilities when treatment was added to the model. The study clearly shows that the transition from normal to depression for police officers is very high: 50.3974026 per cent, and 50.3014325 per cent of transitions from the normal state to the depressed state. This means that the treatment reduces the level of transition probabilities by lowering the depression effect. The study developed a logistic regression model with treatment that was able to reduce the transition probability of depression from 0.9461 to 0.5040.