International Journal of Social Science & Economic Research
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Title:
A NOVEL META-MACHINE LEARNING APPROACH TO DIAGNOSE STRESS FROM INDIVIDUAL FACTORS USING A SELF-RETRIEVED DATASET AND THEN PROVIDE DIRECTED TREATMENT

Authors:
John Leddo

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Nihal Boina, Jai Agarwal, Taruna Agrawal, John Leddo, Monish Napa, Tanya Singhal, Neha Muralitharan, Poorvaja Gopinath, Kinnari Chaubal, Dillon Michlena, Ashwin Tripathy, Kalagee Mehta, Muhammad Siddiqui, SmaranPasupuleti, Riya Pasupuleti, Aneesh Sreedhara, AsvinGopinathan, HavishRallabandi, NikhitRachapudi, Nate Levkov, Alyssa Gatesman, Parth Patel,SathvikRedrouthu, JagadeepramMaddipatla, Vaneesha Gupta, Yash Sonis, Vikram Rudraraju, Aditya Sharma, Aarush Dhawan, Aastha Sharma, Saranya Ganne, Bhargav Subash, Abhiraj Tiwari, Alina Sharifi, Soureen Singh, Alec Agayan, Sauman Das, Arnav Jain, Utkarsh Goyal, Saurav Banerjee, Sadhana Mallemudi, SiddarthMallemudi, Mason Elkas, PrithamMulagura, Kavya Velaga, Nithya Jayakaran, ShriyanBachigari, VedhaBommineni, Sanaa Karkera, SharanyaChilukuri, Aaditya Panjabi, Jishnu Patel, Vihaan Cherukuri, Eeshika Singh, Inaayah Khan
MyEdMaster, LLC, 13750 Sunrise Valley Drive, Herndon, VA, United States of America

MLA 8
Leddo, John. "A NOVEL META-MACHINE LEARNING APPROACH TO DIAGNOSE STRESS FROM INDIVIDUAL FACTORS USING A SELF-RETRIEVED DATASET AND THEN PROVIDE DIRECTED TREATMENT." Int. j. of Social Science and Economic Research, vol. 6, no. 12, Dec. 2021, pp. 4933-4944, doi.org/10.46609/IJSSER.2021.v06i12.031. Accessed Dec. 2021.
APA 6
Leddo, J. (2021, December). A NOVEL META-MACHINE LEARNING APPROACH TO DIAGNOSE STRESS FROM INDIVIDUAL FACTORS USING A SELF-RETRIEVED DATASET AND THEN PROVIDE DIRECTED TREATMENT. Int. j. of Social Science and Economic Research, 6(12), 4933-4944. Retrieved from doi.org/10.46609/IJSSER.2021.v06i12.031
Chicago
Leddo, John. "A NOVEL META-MACHINE LEARNING APPROACH TO DIAGNOSE STRESS FROM INDIVIDUAL FACTORS USING A SELF-RETRIEVED DATASET AND THEN PROVIDE DIRECTED TREATMENT." Int. j. of Social Science and Economic Research 6, no. 12 (December 2021), 4933-4944. Accessed December, 2021. doi.org/10.46609/IJSSER.2021.v06i12.031.

References
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Abstract:
One of the main goals of machine learning is to make a General Artificial Intelligence. Currently, human artificial intelligence researchers work on meticulously manipulating model parameters by hand in order to arrive at highly optimized machine learning models. In the future, a system will be needed such that a software is able to completely arrive at an optimized model to a specific topic all by itself. An increasingly aware human problem is stress, which can oftentimes lead to a variety of health issues. Artificial intelligence (AI) algorithms, specifically Random Forests, have been employed to diagnose potential mental health illnesses due to a particular personal stress. Additionally, these algorithms would be manipulated by an automated hyper parameter manipulator, using extensive machine learning to find, sort, and train, validate, and test on a dataset all by itself. Put simply, we were able to make an automated software capable of making its own state-of-the-art algorithms through a meta-machine learning approach, filling the role of an AI researcher. Additionally, the software was able to achieve consistent overall testing accuracy of at least 90%, quantifying its potential use in diagnosing potential mental illnesses from survey questions identifying potential stressors. Furthermore, our software is able to go one step beyond and take steps to provide potential solutions and resources to benefit the user’s condition.

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