Papers by sumathi kubendran
Integrated Intelligent Research, 2012
Depressive disorder is an illness that involves the body, mood and thoughts. It interferes with d... more Depressive disorder is an illness that involves the body, mood and thoughts. It interferes with daily life, normal functioning and causes pain for both the person with the disorder and those who care about him/her. Severe depression may lead to serious illness or suicide. The most affected sector is the Adolescent Community. The biggest problem in diagnosing and treating depressive disorders is recognizing that someone is suffering from it. As various factors are involved, it is very difficult for the Psychologists to diagnose depressive disorders correctly at an early stage itself. Nowadays, computers are used in assisting Physicians to diagnose diseases and identify correct treatments according to the patient details. In the same way, computers can also be used in assisting psychologists to diagnose mental disorders and identify correct treatments according to the patient details. Various techniques are available to store the expert knowledge and computerize the diagnosis process....
Abstract—Early diagnosis of mental health problems helps
the professionals to treat it at an earl... more Abstract—Early diagnosis of mental health problems helps
the professionals to treat it at an earlier stage and improves the
patients’ quality of life. So, there is an urgent need to treat basic
mental health problems that prevail among children which may
lead to complicated problems, if not treated at an early stage.
Machine learning Techniques are currently well suited for
analyzing medical data and diagnosing the problem. This
research has identified eight machine learning techniques and
has compared their performances on different measures of
accuracy in diagnosing five basic mental health problems. A data
set consisting of sixty cases is collected for training and testing
the performance of the techniques. Twenty-five attributes have
been identified as important for diagnosing the problem from the
documents. The attributes have been reduced by applying
Feature Selection algorithms over the full attribute data set. The
accuracy over the full attribute set and selected attribute set on
various machine learning techniques have been compared. It is
evident from the results that the three classifiers viz., Multilayer
Perceptron, Multiclass Classifier and LAD Tree produced more
accurate results and there is only a slight difference between
their performances over full attribute set and selected attribute
set.
Keywords—Mental Health Diagnosis; Machine Learning;
Prediction; Feature Selection; Basic Mental Health Problems
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Papers by sumathi kubendran
the professionals to treat it at an earlier stage and improves the
patients’ quality of life. So, there is an urgent need to treat basic
mental health problems that prevail among children which may
lead to complicated problems, if not treated at an early stage.
Machine learning Techniques are currently well suited for
analyzing medical data and diagnosing the problem. This
research has identified eight machine learning techniques and
has compared their performances on different measures of
accuracy in diagnosing five basic mental health problems. A data
set consisting of sixty cases is collected for training and testing
the performance of the techniques. Twenty-five attributes have
been identified as important for diagnosing the problem from the
documents. The attributes have been reduced by applying
Feature Selection algorithms over the full attribute data set. The
accuracy over the full attribute set and selected attribute set on
various machine learning techniques have been compared. It is
evident from the results that the three classifiers viz., Multilayer
Perceptron, Multiclass Classifier and LAD Tree produced more
accurate results and there is only a slight difference between
their performances over full attribute set and selected attribute
set.
Keywords—Mental Health Diagnosis; Machine Learning;
Prediction; Feature Selection; Basic Mental Health Problems
the professionals to treat it at an earlier stage and improves the
patients’ quality of life. So, there is an urgent need to treat basic
mental health problems that prevail among children which may
lead to complicated problems, if not treated at an early stage.
Machine learning Techniques are currently well suited for
analyzing medical data and diagnosing the problem. This
research has identified eight machine learning techniques and
has compared their performances on different measures of
accuracy in diagnosing five basic mental health problems. A data
set consisting of sixty cases is collected for training and testing
the performance of the techniques. Twenty-five attributes have
been identified as important for diagnosing the problem from the
documents. The attributes have been reduced by applying
Feature Selection algorithms over the full attribute data set. The
accuracy over the full attribute set and selected attribute set on
various machine learning techniques have been compared. It is
evident from the results that the three classifiers viz., Multilayer
Perceptron, Multiclass Classifier and LAD Tree produced more
accurate results and there is only a slight difference between
their performances over full attribute set and selected attribute
set.
Keywords—Mental Health Diagnosis; Machine Learning;
Prediction; Feature Selection; Basic Mental Health Problems