Team Name:
LockDown
Team Members:
LockDown
By using the open datasets, we predict a group of citizens who has a higher potential to suffer a mental illness based on their age, gender, and ethnicity. Then, we reach out first by providing a simple survey and suggest the right support service quickly.
Try our demo,
https://noahsong.github.io/cuddle/
In New Zealand, one in five people experiences depression at some stage in their life. One of my own family members suffered severe depression. It is extremely common to feel depressed. Yet, it is often very hard to realize your current condition yourself. Even if you realize something is not right about your mental health, it is hard to get help due to lack of motivation, embarrassment, misunderstanding, and fear of the cost of treatment. We envision that we can help more people by predicting their potential mental diseases and show you are not the only one suffering from these. Then, we direct you to all freely available support or government-subsidized services that can help you to get over this mental disease and live your life again.
Cuddle is a mental health service that is designed to guide our citizens to find the right service quickly and easily.
The current Support System expects people to know their current mental status and expect to search for the right service themself. This can be a very daunting task for those who are not feeling 100% mentally.
In contrast, the Cuddle first estimates 5 most commonly occurring mental diseases based on the user's age, gender, and ethnicity. It displays the percentage of people in your group who are also suffering from similar diseases. This helps users to open up their feelings and emotion and encourage them to step out and try to get help. Knowing you are not alone could help users to increase their awareness and reducing their shamefulness. Based on the estimate, Cuddle recommends a simple customized survey to diagnose the disease better. Then, it finally provides the right support service to the patient.
We use two datasets for this project.
One is about Mental Health statistics. Another is about NZ age, gender, and ethnicity population from Census.
Mental Health provide us with the probability of age group given the disease.
We have rearranged it to get probability of getting disease given the age group using Bayes'theorem and probability of age group from Census dataset.
Description of Use Obtain age group distribution, gender group distribution, and ethnic group distribution in NZ. Used those and health datasets to calculate the probability of suffering from mental diseases given age, gender, and ethnicity.
Description of Use We extracted the conditional probabilities of suffering from 17 listed mental disorders based on age, gender, and ethnicity.
Go to Challenge | 5 teams have entered this challenge.
