Requirements

docx

School

Saint Louis University, Baguio City Main Campus - Bonifacio St., Baguio City *

*We aren’t endorsed by this school

Course

BSBTMGT617

Subject

Computer Science

Date

Jun 23, 2024

Type

docx

Pages

Uploaded by hollumeday303

Using Machine Learning on Big Data for Healthcare Communities for Predicting Diseases Objectives  To evaluate healthcare datasets to drawing meaningful results through Predictive modeling, such as basic regression models  To develop a system that detect or predict various sorts of illnesses in a single stage through Streamlit, an inbuilt python module leveraging on Naïve Bayes algorithm, decision tree, random forest, and Support vector machines (SVMs) classifier.  To evaluate the effectiveness of the specific Machine Learning Algorithms adopted in the proposed model for exactness and accuracy in deriving the best results. Research Questions Below are five research questions that would be addressed in the project: How can the predictive models be developed and integrated into the existing healthcare systems to provide timely and accurate predictions of diseases? To achieve this research question, the project could develop a proposed disease prediction system using one or more Machine Learning algorithms, such as Naïve Bayes algorithm, decision tree, random forest, and Support vector machines (SVMs) classifier. The proposed system could be trained and tested using healthcare data gathered from communities, and its predictive accuracy could be evaluated using performance metrics (e.g., accuracy, precision, recall, F1 score). I can also compare the results of the Machine Learning models with the performance of traditional statistical models, such as linear or logistic regression, to assess their respective advantages and limitations. To integrate the predictive models into existing healthcare systems, the project could collaborate with healthcare organizations to identify appropriate platforms for implementing the models. I can also work with healthcare providers to gather feedback and optimize the models to meet their needs. Challenges to implementation may include data privacy concerns, integration with existing systems, and user adoption. What is the predictive accuracy of different Machine Learning algorithms when applied to big data gathered from healthcare communities for predicting diseases? To determine the predictive accuracy of different Machine Learning algorithms, the project could use cross-validation and performance metrics (such as accuracy, precision, recall, and F1 score) to evaluate the performance of each algorithm on the healthcare data. The project could also compare the performance of the Machine Learning algorithms to traditional statistical methods and assess their respective advantages and limitations. How can feature selection techniques be used to identify the most important variables in the healthcare data that are predictive of diseases, and how does this affect the accuracy of the predictive models? To identify the most important variables in the healthcare data, the project could use feature selection techniques such as recursive feature elimination or principal component analysis. The project could also experiment with different subsets of the data to evaluate how the accuracy of the models is affected. Additionally, the project could investigate how the choice of feature selection technique impacts the accuracy and interpretability of the predictive models.

What are the ethical considerations surrounding the use of Machine Learning on big data from healthcare communities for diseases prediction? To address ethical considerations surrounding the use of Machine Learning on healthcare data, the project could work with healthcare organizations and privacy experts to ensure that data is collected and used in a responsible and ethical manner. This could include obtaining informed consent from patients, implementing appropriate data security measures, and minimizing the risk of bias and discrimination in the models. The project could also investigate the potential societal impact of the predictive models and work to address any unintended consequences. How can deep learning techniques (such as Naïve Bayes algorithm, decision tree, random forest, and Support vector machines (SVMs) classifier) be used to improve the accuracy of diseases prediction on big data gathered from healthcare communities? To use deep learning techniques to improve the accuracy of disease prediction, the project could experiment with different neural network architectures such as Naïve Bayes algorithm, decision tree, random forest, and Support vector machines (SVMs) classifier. The project could also investigate how transfer learning, ensemble methods, and other techniques could be used to improve the accuracy of the models. Challenges to implementing these techniques may include the need for large amounts of training data, longer training times, and the need for specialized hardware. How the Questions Aid demonstration of my Computing Skills 1. To integrate the predictive models into existing healthcare systems and provide timely and accurate predictions of diseases: this would allow me demonstrate my proficiency in integrating Machine Learning models with existing software platforms using technologies like API and database management system. 2. To use deep learning techniques, such as Naïve Bayes algorithm, decision tree, random forest, and Support vector machines (SVMs) classifier, to improve the accuracy of disease prediction on big data gathered from healthcare communities: showcasing computing skills in this objective could involve my proficiency in deep learning architectures, such as Naïve Bayes algorithm, decision tree, random forest, and Support vector machines (SVMs) classifier. Additionally, knowledge of GPU computing and cloud services is necessary to handle the large amounts of data needed for training and testing deep learning models. 3. To evaluate healthcare datasets and draw meaningful results through predictive modeling: In regards to this objective, skills that I will be able to showcase includes proficiency in data preprocessing, feature selection, and model selection techniques. 4. To develop a system that detects or predicts various sorts of illnesses through Streamlit and Machine Learning algorithms: this objective allows me show computing skills in Python programming, including proficiency in using Scikit-learn or TensorFlow libraries to build and train Machine Learning models. Additionally, proficiency in Streamlit, an in-built python module, is necessary to create a functional and responsive user interface. 5. To evaluate the effectiveness of specific Machine Learning algorithms in the proposed model for exactness and accuracy in deriving the best results: Demonstrating computing skills in this objective could involve proficiency in comparing different Machine Learning algorithms' performance in terms of accuracy, precision, recall, and F1 score. It might also involve

proficiency in optimization techniques such as grid search or random search to fine-tune model hyperparameters. How the New Idea will be Evaluated  To use deep learning techniques, such as Naïve Bayes algorithm, decision tree, random forest, and Support vector machines (SVMs), to improve the accuracy of disease prediction on big data gathered from healthcare communities: The new idea could be evaluated based on the ability of deep learning techniques to improve the accuracy of disease prediction on big data gathered from healthcare communities. I can compare the performance of the proposed deep learning models with those of other models and select the best model based on its performance. I can also assess the feasibility of using deep learning models in a production environment, given the computational requirements and other constraints.  To evaluate healthcare datasets and draw meaningful results through predictive modeling: The new idea could be evaluated based on its ability to draw meaningful insights from healthcare datasets that can inform decision-making or identify patterns in healthcare data that may not be apparent through traditional statistical analysis like regression and correlation models.  To develop a system that detects or predicts various sorts of illnesses through Streamlit and Machine Learning algorithms: The new idea could be evaluated based on the system's ability to detect or predict various sorts of illnesses accurately and efficiently. I can also assess the usability and user-friendliness of the system through user testing and feedback.  To evaluate the effectiveness of specific Machine Learning algorithms in the proposed model for exactness and accuracy in deriving the best results: The new idea could be evaluated based on its ability to improve the accuracy and precision of predictive models using specific Machine Learning algorithms. I can compare the performance of the proposed models with those of other models and select the best model based on its performance.  To integrate the predictive models into existing healthcare systems and provide timely and accurate predictions of diseases: The new idea could be evaluated based on its ability to integrate Machine Learning models with existing healthcare systems and provide accurate predictions of diseases in real-time. I can test the system's performance in a controlled environment and collect feedback from stakeholders to improve the system's usability.

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Related Documents

D212_JBT_Task2.pdf

CIS 4510 Discussion 7.docx

Chapter 7_.docx

CYB 200 Module Four Activity Template_KOCH.docx

Lab 4 - CIS Control 01 - Vulnerability Scanning and Asset Discovery Tool (Active Scanning).docx

ABA 624 WEEK 1 SAFMEDS.docx

ABA 624 WEEK 2 SAFMEDS.docx

The K-mean Clustering Analysis .docx

BUSINESS 13 - Machine Learning Practice Quiz.docx

Lab-3 forensics.docx

Hack the box.docx

Assignment1 P1, CPSC5207E Virtualization and s24 v7.pdf

Recommended textbooks for you

Operations Research : Applications and Algorithms

Computer Science

ISBN:9780534380588

Author:Wayne L. Winston

Publisher:Brooks Cole

Principles of Information Systems (MindTap Course...

Computer Science

ISBN:9781285867168

Author:Ralph Stair, George Reynolds

Publisher:Cengage Learning

MIS

Computer Science

ISBN:9781337681919

Author:BIDGOLI

Publisher:Cengage

Fundamentals of Information Systems

Computer Science

ISBN:9781305082168

Author:Ralph Stair, George Reynolds

Publisher:Cengage Learning

Fundamentals of Information Systems

Computer Science

ISBN:9781337097536

Author:Ralph Stair, George Reynolds

Publisher:Cengage Learning

Principles of Information Systems (MindTap Course...

Computer Science

ISBN:9781305971776

Author:Ralph Stair, George Reynolds

Publisher:Cengage Learning

SEE MORE TEXTBOOKS

Recommended textbooks for you

Operations Research : Applications and Algorithms
Computer Science
ISBN:9780534380588
Author:Wayne L. Winston
Publisher:Brooks Cole
Principles of Information Systems (MindTap Course...
Computer Science
ISBN:9781285867168
Author:Ralph Stair, George Reynolds
Publisher:Cengage Learning
MIS
Computer Science
ISBN:9781337681919
Author:BIDGOLI
Publisher:Cengage
Fundamentals of Information Systems
Computer Science
ISBN:9781305082168
Author:Ralph Stair, George Reynolds
Publisher:Cengage Learning
Fundamentals of Information Systems
Computer Science
ISBN:9781337097536
Author:Ralph Stair, George Reynolds
Publisher:Cengage Learning
Principles of Information Systems (MindTap Course...
Computer Science
ISBN:9781305971776
Author:Ralph Stair, George Reynolds
Publisher:Cengage Learning