Authors :
Dr. P. Bhaskar Naidu; Mannam Ruchitha; Pandilla Yaswanth; Battula Harika; Pamidi Prabhu; Gadiraju Venkata Deepthi Sree
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/yky8wbvy
Scribd :
https://tinyurl.com/u8muc74x
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR701
Abstract :
We use of random forest algorithm, which is
an ML calculation, for the recognition of emotional well-
being conditions. Emotional well-being problems present
critical difficulties around the world, with early
discovery being essential for successful mediation and
treatment. Utilizing information from different sources,
for example, online entertainment, electronic wellbeing
records, and self-revealed studies. Random forest offers
a powerful structure for prescient demonstrating. By
breaking down an assorted arrangement of elements
including etymological examples, conduct signals, and
segment data, random forest can successfully order
people into various psychological well-being classes like
melancholy, uneasiness, and stress. The gathering idea of
Arbitrary Woods empowers it to deal with complex
connections inside the information, yielding solid
forecasts even within sight of commotion and exceptions.
Through thorough preparation and approval
methodologies, we exhibit the adequacy of random forest
in precisely recognizing people in danger of
psychological wellness problems. This approach holds
guarantees for versatile and available emotional wellness
screening, empowering ideal mediations, and backing for
those out of luck. As we dive further into the domain of
ML applications in psychological well-being, random
forest arises as a significant device for upgrading our
comprehension and understanding of these
circumstances.
Keywords :
Emotional Well-Being, Machine Learning, Random Forest Algorithm, Psychological.
We use of random forest algorithm, which is
an ML calculation, for the recognition of emotional well-
being conditions. Emotional well-being problems present
critical difficulties around the world, with early
discovery being essential for successful mediation and
treatment. Utilizing information from different sources,
for example, online entertainment, electronic wellbeing
records, and self-revealed studies. Random forest offers
a powerful structure for prescient demonstrating. By
breaking down an assorted arrangement of elements
including etymological examples, conduct signals, and
segment data, random forest can successfully order
people into various psychological well-being classes like
melancholy, uneasiness, and stress. The gathering idea of
Arbitrary Woods empowers it to deal with complex
connections inside the information, yielding solid
forecasts even within sight of commotion and exceptions.
Through thorough preparation and approval
methodologies, we exhibit the adequacy of random forest
in precisely recognizing people in danger of
psychological wellness problems. This approach holds
guarantees for versatile and available emotional wellness
screening, empowering ideal mediations, and backing for
those out of luck. As we dive further into the domain of
ML applications in psychological well-being, random
forest arises as a significant device for upgrading our
comprehension and understanding of these
circumstances.
Keywords :
Emotional Well-Being, Machine Learning, Random Forest Algorithm, Psychological.
