Authors

1. Ms. Suchita Sinhal
2. Dr. Ruchi Nanda

Affiliation

• Research Scholar, CS & IT, The IIS University, Jaipur
• Sr. Assistant Professor, CS & IT, The IIS University, Jaipur

Abstract

Data mining and Data analytics are promising research fields for their attempts to predict and analyze
data from different perceptions and summarize it into significant information in order to identify hidden
patterns from a huge dataset. Data mining identifies patterns which were earlier undetected by using
statistical approach whereas data analytics focuses on comparing the patterns discovered with other
patterns to solve business problems. These are equally significant and connected fields that cannot
exist without each other. Healthcare organizations are exploiting these technologies for improvising
their clinical and business processes. The healthcare data related to mental health is quite complex
and uneven. The state of depression and mental disorders are neglected due to unpredictable
symptoms and treatments based on assumptions. Depression is one of the most common problems
that affect a large population today and is noticeable in any age group.
This paper provides the description of various data mining techniques such as Artificial Neural
Networks, Decision Trees, Fuzzy classifiers and Bayesian classifiers applied to massive volume of
depression data. This paper comprehensively presents a literature review on these techniques
utilized by the researchers in the prediction of depression. It helps in investigating the factors
responsible for depression. These factors are perceived and mathematically modeled. The paper
provides an insight into some of the commonly implemented data mining techniques used to detect
depression as well as classify the depression in different states and make a comparative study of
such techniques based on their performance and accuracy. An estimate of the data size required to
predict and detect depression is presented that can help in future research work. It explains why
some techniques are more commonly used as compared to others by highlighting the rationale. This
paper also helps in finding out the gap in the existing prediction techniques which helps researchers
to further improvise the techniques by using some advanced methods and functions that can be
applied for the prediction of depression.

Keywords

Data-Mining, Health-Care, Mental-Health, Depression, ANN, Bayesian Classifiers, Decision Trees, Fuzzy Classifiers

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