Using Artificial Intelligence to help solve mental health issues can provide important benefits. AI tools integrated into healthcare websites can replicate therapist conversations to assess patients and provide feedback. AI can also be used on social media to help people feel more connected, reducing isolation. Both supervised and unsupervised learning techniques can be applied. Challenges include addressing biases in algorithms and limited resources, but these can be overcome with stakeholder input and community support. Accuracy and precision are appropriate metrics to evaluate how well an AI system classifies different mental disorders.

1. Using Artificial Intelligence for
mind/mental well-being
WITH INCREASED MENTAL ILLNESSES, USING ARTIFICIAL INTELLIGENCE TO SOLVE
UNDERLYING ISSUES ON MENTAL HEALTH IS AN ESSENTIAL ROLE TECHNOLOGY CAN
OFFER TO THE COMMUNITY ESPECIALLY THE SICK PEOPLE GOING THROUGH MENTAL
ILLNESSES. AS A WAY TO BRING SERVICES CLOSER TO THEM THROUGH AVAILABLE
DEVICES AND SYSTEMS SINCE IT IS CHEAPER AND ACCURATE ENOUGH TO HELP THE
HEALTHCARE SYSTEM TO OFFER PROPER MEDICATION AND COUNSELLING, AND ALL
OTHER SUPPORT, FOR THE WELL-BEING OF THE MENTALLY SICK PEOPLE.

2. How Artificial Intelligence can be applied here
 Artificial Intelligence can be applied through building integrated systems or
(CBT) - cognitive behavior therapy tools on healthcare websites that can be
used to replicate conversations a patient might have with his or her therapist.
Such a tool(s) can be used to emulate real-life face-to-face meeting with a
patient, the system asks how the patient feels, how their day was, and assess,
basing on the data to provide feedback on the current situation of the patient
for immediate response.
 Social networking has proved to be one of the main reasons why people need
to be happy, loved and have a feeling of belonging to the people they relate
with, making them mentally healthy. Artificial Intelligence tools can be used
on some internet tools that can be used to make people feel more connected
to each other and avoid being separated from each other for long.

3. Type of Data to be used for this problem.
 Social networking data of all the people to be checked. As this type of data will help to
study the relations of the suspected patients and be able to analysed thoroughly. This can
be the type of messages used, responses to look out for expected cases of suicidal
thoughts and many more.
 Human memories and thoughts observed through interactions with their peers via
different platforms where they exchange such thoughts.
 Past Medical backgrounds/history/records from hospital databases about a group of
people or a person suspected to have gone through a mental health situation.
 The type of data to be used in this project has to be descriptive (describing the mental
problem at hand) and predictive (easily predictability of the occurrences of mental health
issues).

4. The Differences Between Supervised and
Unsupervised Learning.
Supervised learning
 In Supervised learning, you train the
machine using a module with labelled
data.
 Supervised learning allows you to collect
data or produce a data output from the
previous experience.
 Regression and Classification are the types
involved in a supervised machine learning
technique.
 Supervised learning is a simpler method.
Unsupervised learning
 In Unsupervised learning, you do not need to
train or supervise the machine using a module
with labelled data.
 Unsupervised machine learning helps you to
finds all unknown patterns in your data that you
get to feed into your machine unsupervised.
 Clustering and Association are the other two
types of Unsupervised learning as well.
 Unsupervised learning is computationally
complex

5. Supervised Learning Techniques to be used
to solve mental illnesses
Regression:
Regression is the technique that can
be used to predict a single output
value using or from training data.
With this technique, a machine can be fed
with data about the mental illnesses in a
year in a locality and be used to determine
whether there would be an increase or
decrease in the rates of mental health.
(Predictions basing on the data used to
train the machine.
Classification:
Classification means to group the
output inside a class. If the algorithm
tries to label input into two distinct
classes, it is called binary
classification.
With this technique, a machine can be
used to determine whether someone will
have mental illnesses in a given
period/time or not.

6. Unsupervised Learning Techniques to be
used to solve mental illnesses
 Clustering:
This is a technique that is usually used in
uncategorized data. From this uncategorized
data, an algorithm can be used or is used to find
a pattern or collection basing on the data
available. Under mental illnesses, the machine is
able or is programmed with an algorithm to
determine patterns of the underlying mental
health issues, basing on age, gender and many
other factors and finds patterns relating to each
of these small groups of data.
 Association:
This is a technique that allows you to establish
associations amongst data objects inside large
databases. This unsupervised technique is
about discovering relationships between
variables in large databases. For example,
under the problem statement, this technique
can be used to make subgroups of mental
health patients grouped by their gene
expression measurements.

7. Artificial Intelligence challenges likely to be
faced in this project
 Algorithms are created by people who have their own values, morals, assumptions, and
explicit and implicit biases about the world, and those biases can influence the way Artificial
Intelligence models function. This can be a problem as the system maybe trained through an
algorithm that is a bit biased basing on a group of people or a person making the system
inaccurate.
 The Limited resource in terms of skilled labor, financial stability that will have to facilitate the
smooth running of the operations of this project. Without such resources, the project can
take long to be operative or even never operate despite the ideas to embark on solving
mental health illnesses in the area.
 Limited support from the community making it hard to track the patients with a particular
mental disorder.
 Limited sensitization of the public on the efforts to use technology to help solve the issues
of mental illnesses in the community.

8. The Possible Solutions to the above
project challenges.
 Algorithms written are supposed or can be scrutinized by a group of professionals,
software engineers and health experts so as to be able to entail everything
needed for the algorithm to work neutral, without being biased on one person’s
thinking about a given mental health issue.
 Provision of unlimited access to relevant and necessary resources needed to run
the project, such as financial support, skilled personnel, government support so as
to be able to capture a wide range of people with such mental problems with
easy, and at all costs, as a way of ensuring mental health in a given community.
 Adequate community sensitization and mobilization on the importance and
efforts carried out towards ensuring a healthy mind (good mental health).

9. Differences between Accuracy, Recall,
Precision and F1-Score.
 Precision : It is the ratio of system generated results that correctly predicted positive observations (True Positives) to the
system’s total predicted positive observations, both correct (True Positives) and incorrect (False Positives). For example: How
many of those mental illnesses labelled by the system as insomnia are actually insomnia?
 Accuracy: Accuracy is an evaluation metric that determines the number of correct predictions made by the model, for
example, How many mental issues did the system correctly classify (i.e. both True Positives or True Negatives) out of all the
mental illnesses?
 Recall (Sensitivity): It is the ratio of system generated results that correctly predicted positive observations (True
Positives) to all observations in the actual malignant class (Actual Positives), for example: Of all the mental illnesses that are
insomnia, how many of those did the system correctly classify as insomnia?
 F1-Score: It is the weighted average (or harmonic mean) of Precision and Recall. Therefore, this score takes both False
Positives and False Negatives into account to strike a balance between precision and Recall.

10. Two most appropriate Artificial Intelligence
evaluation metrics for this project.
 Accuracy :
Accuracy is the ratio of the number of correct predictions to the total number of input samples. It works well
only if there are equal number of samples belonging to each class. Here the system is tested to see among all
the mental disorder classifications, whether all those classifications were correctly classified, for example, were
the insomnia disorders classified correctly under a given class.
 Precision :
It is the ratio of system generated results that correctly predicted positive observations to the system’s
total predicted positive observations, both correct and incorrect. For example: How many of those mental
illnesses labelled by the system as insomnia are actually insomnia?. Here we can use the Artificial Intelligence
(AI) tool to actually assess whether the information it produces (or labels the disorder) about the disorder is
actually that disorder, given the data provided. The system creates a classifier, which is an algorithm that learns
the data and detects whether something belongs to a given class. So here, the classifier is actually tested to see
if a given disorder it has detected belongs to a given class it has classified it (disorder - insomnia) into.

11. The Work (Project) References
 Master’s in Data Science => https://www.mastersindatascience.org/resources/how-data-science-
can-improve-mental-health-care/
 Very well health => https://www.verywellhealth.com/using-artificial-intelligence-for-mental-health-
4144239
 guru99.com => https://www.guru99.com/supervised-vs-unsupervised-learning.html
 American Psychological Association => https://www.apa.org/monitor/2021/11/cover-artificial-
intelligence
 Lawtomated => https://lawtomated.com/accuracy-precision-recall-and-f1-scores-for-lawyers/