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Title |
A Predictive Model to identify possible affected Bipolar disorder students using Na?ve Baye’s, Random Forest and SVM machine learning techniques of data mining and Building a Sequential Deep Learning Model using Keras |
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Author |
S. Peerbasha1*, M. Mohamed Surputheen |
| Citation |
Vol. 21 No. 5 pp. 267-274 |
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Abstract |
Medical care practices include gathering a wide range of student data that are with manic episodes and depression which would assist the specialist with diagnosing a health condition of the students correctly. In this way, the instructors of the specific students will also identify those students and take care of them well. The data which we collected from the students could be straightforward indications seen by them. The artificial intelligence has been utilized with Naive Baye’s classification, Random forest classification algorithm, SVM algorithm to characterize the datasets which we gathered to check whether the student is influenced by Bipolar illness or not. Performance analysis of the disease data for the algorithms used is calculated and compared. Also, a sequential deep learning model is builded using Keras. The consequences of the simulations show the efficacy of the grouping techniques on a dataset, just as the nature and complexity of the dataset utilized. |
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Keywords |
Artificial intelligence · Bipolar illness · Data mining techniques · Na?ve Baye’s classification · Random forest classification ? SVM classification |
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URL |
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Title |
A Predictive Model to identify possible affected Bipolar disorder students using Na?ve Baye’s, Random Forest and SVM machine learning techniques of data mining and Building a Sequential Deep Learning Model using Keras |
|
Author |
S. Peerbasha1*, M. Mohamed Surputheen |
| Citation |
Vol. 21 No. 5 pp. 267-274 |
|
Abstract |
Medical care practices include gathering a wide range of student data that are with manic episodes and depression which would assist the specialist with diagnosing a health condition of the students correctly. In this way, the instructors of the specific students will also identify those students and take care of them well. The data which we collected from the students could be straightforward indications seen by them. The artificial intelligence has been utilized with Naive Baye’s classification, Random forest classification algorithm, SVM algorithm to characterize the datasets which we gathered to check whether the student is influenced by Bipolar illness or not. Performance analysis of the disease data for the algorithms used is calculated and compared. Also, a sequential deep learning model is builded using Keras. The consequences of the simulations show the efficacy of the grouping techniques on a dataset, just as the nature and complexity of the dataset utilized. |
|
Keywords |
Artificial intelligence · Bipolar illness · Data mining techniques · Na?ve Baye’s classification · Random forest classification ? SVM classification |
|
URL |
