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

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BSBTMGT617

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Computer Science

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Jun 23, 2024

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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

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