This document describes a health care chatbot developed using natural language processing. The chatbot was created to provide medical information and assistance to patients, especially in emergency situations. The authors used techniques like tokenization, stop word removal, and word similarity analysis. They tested different learning rates and optimizers (SGD, Adam) and found Adam with a learning rate of 0.0099 produced the best results with 93% accuracy. The chatbot was implemented in Python using common NLP libraries and could help address issues like limited doctor availability.

1. 2022 IEEE World Conference on Applied Intelligence and Computing
Health Care Chatbot using Natural Language Processing
with SGD and ADAM Optimizer Parameter Optimization
(Paper ID-587)
Authors Name
Kailash Chandra Bandhu, Binod Kumar Mishra, Narottam Choyal, Prakhar Varathe,
Mohit Patel & Priya Koushal
Department of Computer Science and Engineering
Medi-Caps University
Indore, India
Presented by
Binod Kumar Mishra & Kailash Chandra Bandhu
1

2. Contents
• Abstract
• Introduction
• Literature Survey
• Methodology
• Implementation Output
• Result
• Conclusion
• References
2

3. Abstract
In today’s world, everyone is not quite sure about the medicine that the users used in a
similar situation or critical situation where any medical emergency has come and as all
know that the ratio of patients and doctors are very high so, there is a requirement of
such kind of applications to help in case of emergency.
This paper proposed a novel approach for medical needs, as well as the suggested
chatbot that will be useful in the pandemic circumstances. Natural Language
Processing (NLP) based applications are proposed to provide help to the patient. In
some situations, the patient home member just used it to type their query and if the
patient situation is not so serious, so they get proper medicinal information from this
application.
The proposed methodology takes an input sentence then its tokenization, removal of
stop words, feature extraction, and word corpus are used to find the sentence
similarity, and the chatbot predicts the accurate sentence.
Keywords: Chatbot, Healthcare, Natural Language Processing, Emergency, Patient,
Doctor.
3

4. Introduction
• Chatbot is a computer program that simulates human conversion
through Artificial Intelligence.
• Natural Language Processing (NLP) is an essential component of
Artificial Intelligence that can understand human language and makes
computers perform useful tasks with human language.
• Current approaches of NLP are based on Machine Learning.
• The input and output of an NLP can be Text or Speech.
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5. Need for Chatbot
• Widespread use of personal machines.
• Better Human-Computer Interaction.
• To express their interest, wishes, or queries directly and naturally, by
speaking, typing, and pointing.
• Due to the pandemic situation Doctors as well as patients avoid
visiting.
5

6. Literature Survey
• ELIZA & PARRY
• Developed in the 1960 and 1972s
• Simulated a paranoid schizophrenic
• Chatbot for Psychiatric Counseling in Mental Healthcare Service
Based on Emotional Dialogue Analysis and Sentence Generation
[1].
6

7. Literature Survey
Contd..
• Oh et al. (2017) [2], provides chat service for psychiatric counseling based on high-level natural language
understanding and a multi-modal approach. This study is based on the perception of emotional separation
using artificial intelligence methods.
• Setiaji et al. (2016) [3], implement a chatbot to create a conversation between a person and a machine.
The information is stored in database for identifying the correct sentence and predicting the answer of the
question.
• Dahiya (2017) [4], developed a chatbot that is used pattern comparisons where sentence structure is
detected and a response pattern is maintained.
• Shabariram et al. (2017) [5], build a chatbot application using the XML-based dataset, and the K-Means
is used for clustering with required attributes.
• Dharwadkar et al. (2018) [6], have built a chatbot for health care purposes for the android operating
system.
• Naaz et al. (2017) [7], work used the N-Gram-based and low-dimensional demonstration (LSI-SVD)
approach for detecting duplicate and closest to duplicate documents and it is implemented using c#.net.
• Hormansyah et al. (2018) [8], Designed the chatbot for customer service as a public health service. N-
gram, TF-IDF, and cosine similarity are used in the programme
• Gupta et al. (2021) [14] built a chatbot using RASA framework. This system takes symptoms as input and
predicts the disease with recommended treatments.
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8. Methodology
8

9. Methodology
Contd..
9

10. Implementation Output
10

11. Result
• Since our work related to the optimizer for Neural Network.
We used Adam and SGD for this thing.
The optimization algorithm (or optimizer) is the main approach used
today for training a machine learning model to minimize its error
rate.
• There are two metrics to determine the efficacy of an optimizer:
speed of convergence (the process of reaching a global optimum for
gradient descent); and generalization (the model’s performance on
new data). Popular algorithms such as Adaptive Moment Estimation
(Adam) or Stochastic Gradient Descent (SGD) can capably cover one
or the other metric.
11

12. Result
Contd..
• Gradient descent is the most common
method used to optimize deep learning
networks. It can update each parameter of
a model, observe how a change would
affect the objective function, choose a
direction that would lower the error rate,
and continue iterating until the objective
function converges to the minimum.
• SGD is a variant of gradient descent.
Instead of performing computations on
the whole dataset - which is redundant
and inefficient - SGD only computes on a
small subset or random selection of data
examples. SGD produces the same
performance as regular gradient descent
when the learning rate is low.
• Essentially Adam is an algorithm for
gradient-based optimization of stochastic
objective functions. It combines the
advantages of two SGD extensions - Root
Mean Square Propagation (RMSProp) and
Adaptive Gradient Algorithm (AdaGrad) -
and computes individual adaptive learning
rates for different parameters.
12

13. Result
Contd..
Lr-value Decay
Responses out of
15
Accuracy Status
0.01 1e-6 8 53% Average
0.05 1e-6 0 0% Wrong Response
1 1e-6 0 0% No Response
1.01 1e-6 Error Nil Error
1.05 1e-6 Out of range Nil Out of Range
0.001 1e-6 7 46% Alternate Correct Response
0.099 1e-6 0 0% No Response
0.0099 1e-6 11 73% Moderate
0.01 1e-10 8 53% Average
0.05 1e-10 0 0% No Answer
1 1e-10 Error Nil Error
1.01 1e-10 0 0% No Response
1.05 1e-10 0 0% No Response
0.001 1e-10 7 46% Not Feasible
0.099 1e-10 0 0% No Response
0.0099 1e-10 13 86% Better
SGD Optimizer 13

14. Result Contd..
Learning_rate Responses out of 15 Accuracy Status
0.01 13 86% Better
0.05 1 6% No Response
1 0 0% No Response
1.01 0 0% No Response
1.05 0 0% No Response
0.001 Error Nil Index Error
0.099 0 0% No Response
0.0099 14 93% Best
Adam Optimizer
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15. Result
Contd..
Comparison between SGD and ADAM optimizers
Optimizers Learning rate Accuracy
SGD 0.0099 86%
SGD 0.01 53%
ADAM 0.0099 93%
ADAM 0.01 86%
ADAGRAD [Desai
(2020)] [18]
0.01 89.63%
15

16. Conclusion
• Chatbot was developed using a deep neural network with an Adam
optimizer learning rate of 0.0099 is better than other approaches.
• Simple Chatbot develop using Python and NLTK library.
• Work can be extended if we add some security features also data
privacy for the patients.
• For better satisfactory results, we can add additional features to
recognize the human emotions and sentiments of the patient so that
our machine can predict actual problems and solutions.
16

17. References
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2. K. Oh, D. Lee, B. Ko and H. Choi, "A Chatbot for Psychiatric Counseling in Mental Healthcare Service Based on Emotional Dialogue Analysis and Sentence Generation," 2017
18th IEEE International Conference on Mobile Data Management (MDM), 2017, pp. 371-375, https://doi.org/10.1109/MDM.2017.64.
3. B. Setiaji and F. W. Wibowo, “Chatbot Using a Knowledge in Database: Human-to-Machine Conversation Modeling,” 2016 7th International Conference on Intelligent Systems,
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Volume-5, Issue-5, E-ISSN: 2347-2693, pp 158-161.
5. C.P. Shabariram, V. Srinath, C.S. Indhuja and M. Vidhya, “Ratatta: Chatbot Application Using Expert System,” International Journal of Advanced Research in Computer Science
and Software Engineering, 2017.
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18. References
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19. Thank You
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