Weighting: The Project Document (20%) Coursework

Coursework 1

Weighting: The Project Document (20%) Coursework

Hand in date: 21st June 2024 (11:59am)

Learning Outcomes Assessed:

LO1 Identify, specify and critically analyse a system, issue, or problem of current interest within a relevant context.

LO2 Critically and systematically review relevant literature and alternative approaches and solutions.

LO3 Demonstrate and critically self-reflect on the significance of the outcomes of the project in a professional manner including aspects related to legal, social, and ethical implications.

IntroductionThis assignment should demonstrate what problem the project is addressing and how it will be solved and evaluated. The emphasis of this report should be on what work has been done so far. This report should contain a critical analysis of a system, issue, or a problem, with aim and objectives. It is also expected that the progress against a plan is reported and analysed.

The reported progress should be evidenced by interim deliverable that will contribute towards the final project. For example, this could be an analysis of alternative methods of solution; a feasibility study; a prototype, a literature review, etc. which will be included as appendices.

A typical Interim Report will be approximately 3000 words, excluding tables, figures, project plan, references, and appendices.

General Guidance

The aim of the interim report layout should be to make it as easy as possible for somebody to read and understand your work - it is obviously in your own interests to ensure that this is the case.

Format

Margins: Standard margins are typically 1 inch on all sides. This helps ensure the text is well-framed on the page and looks tidy.

Spacing: Use 1.5 or double line spacing for the main text. This makes the report easier to read.

Alignment: Text should be left-aligned.

Font

Type: Choose a clear, professional font. Common choices include Times New Roman, Arial, and Calibri because of their readability.

Size: Typically, 12-point font is used for the main text, with larger sizes for headings and subheadings to distinguish them clearly from the main body.

Consistency: Keep the font consistent throughout the document, except in figures and tables if necessary for clarity.

Recommended Contents of the Report

Interim Report must be written in 3rd person and should contain:

Title Page

Table of Contents Page (List of Tables and List of Figures may be included if necessary)

Chapter 1 Introduction

Chapter 2 Background Research / Literature Review

Chapter 3 Legal Social and Ethical implications

Chapter 4 Project Management & Progress Review

References

Title page

This should provide sufficient information to indicate the contents of the Report. It must contain the following information:

Title of the project

Student's name.Supervisors name.

Date of Submission.

Chapter 1. Introduction

Assume that your readers have little knowledge of the subject and introduce the subject to them. This is where you provide the rationale for your project idea and introduce your readers to the topic and to clarify the context of your project. For example, what problem are you trying to solve through the use of technology? If your project is about the creation of a piece of software for a particular issue, then write a short introduction to software quality and the area being targeted - sufficient so that your readers will understand the general area of your project. (approx. 250 words)

This chapter should also identify the aim of the project and what objectives you will have to achieve to successfully complete the project. You should have one aim (singular) and a number of objectives (plural). Your aim should clarify what the overall achievement is intended to be and should reflect the title of your work. You should then have a list of measurable objectives (SMART). (approx. 150 words)

Chapter 2. Project Background and Literature review

The project background expands on what was said in the introduction about the topic and clarifies the area of your project. Key to this section is a literature / organisation / product review of other people's work and an understanding of how technology already addresses (or fails to address) what you are proposing. (approx. 1000-1500 words)

Chapter 3. Ethical consideration

Consider ethical implications this project might have. Identify who you are going to affect with regards to personal information, additional circumstances related to the users i.e. additional needs / users at risk / vulnerable people and how you are going to protect their interests when the artefact is evaluated and up and running. (approx. 250-300 words)

Chapter 4. Project Management & Progress Review

Project management involves dissecting the project's workload into phases, tasks, and various activities, along with time estimations for completing each aspect, encapsulated within a project plan. This plan sets out task interdependencies, critical work components, and the overall schedule. Additionally, it outlines the methodological approach adopted for project implementation and completion. It is essential to detail the progress achieved so far and identify outstanding tasks yet to be completed. (consider using a Gantt chart or similar)

You should also provide a critical reflective analysis of your performance in relation to the plan, the project objectives and overall aim. (approx. 250 - 300 words)

References

Much of what you present will have been touched on, discussed, written about, or covered by other people in the past (Chapter 2 and 3 in particular). Therefore, your discussions must be adequately referenced using EHU Harvard format:

Material is referenced within reports primarily to:

Avoid plagiarism - you do not present other peoples work, ideas, thoughts, words, figures, diagrams, results and so on to make their work look as if it is your own.

CIS4517 Research and Development Project

Template for the project proposal

1. Your Name Hasnain 2. Course title

Msc data Science and Artificial Intelligence

3. Email

4. Module code and name RESEARCH AND DEVELOPMENT PROJECT 2023

5. Names of potential project supervisors

(up to three in the descending order of preference) 6. Title or topic area and main research question of your proposed study

Machine Learning Models for Symptom Prediction in Disease Diagnosis

7. What are the aim and objectives of your study?

Aim

To develop a reliable method for the early detection of symptoms associated with diseases such as cancer and diabetes, with the goal of enabling timely and effective treatment for patients.

Objectives

To develop predictive models that accurately identify symptom patterns of cancer and diabetes based on genetic factors, medical history, and lifestyle factors.

To validate these predictive models through rigorous testing using diverse and representative datasets.

To refine the predictive models by analyzing the impact of genetic factors, medical history, and lifestyle on the accuracy of symptom prediction.

8. Focused review of relevant literature, public datasets and open source packages and rationale for study (at the end of this section, list 8-10 key references)

Predictive analytics to diagnose the disease with the help of machine learning (ML) and artificial intelligence (AI) in order to identify different patterns in medical data helps us detecting the diseases early and accurate. This technique is very much good for some dangerous or non-curable diseases at the later stages such as cancer and diabetes, where early diagnosis can help in improving the expected outcomes of the patient. This literature review highlights some literature points by observing the public datasets, and some other open-source surveys that helps in predicting analytics to diagnose the disease.

Literature review

Machine Learning in Healthcare

According to Topol, E. J. (2019), he has discussed about the deep medicines and how Artificial Intelligence can make the healthcare human again. He has also discussed in his writing about the transformative potential of AI in the domain of healthcare, and also enhancing the role of predictive analytics in the very early disease diagnosis.

According to Esteva et al., (2017) discussed about the Dermatologist-level classification of skin cancer with deep neural networks. This study helps in demonstrating the capability of models of the deep learning in order to classify the skin cancer with accuracy comparable related to dermatologists.

Specific Disease Prediction

According to Rajkomar et al., (2018), they have discussed about the Scalable as well as accurate deep learning within the electronic health records. This research helps in highlighting the applications and uses of deep learning within the electronic health records (EHRs) for predicting the patient outcomes; it also helps in showcasing scalability and accuracy.

As per, Choi et al., (2016) discussed about the Doctor AI; they have also predicted the models by predicting clinical events through recurrent neural networks. They have also discussed about the Machine Learning for Healthcare Conference. This paper also discussed about various Doctor AI which is built by using a model using recurrent neural networks (RNNs) to predict the clinical events that helps in using the temporal data for appropriate as well as accurate predictions.

Methodological Advances

According to Miotto et al., (2017) discussed about the Deep learning for healthcare they have discussed about the opportunities and challenges. This paper review and it also provides an overview of deep learning applications with in the healthcare also discussed the challenges as well as potential solutions.

As per, Shickel et al., (2018), this paper discussed about the Deep HER which a survey on the latest and different advances on deep learning techniques for electronic health record-based clinical prediction. This paper also highlights recent advances in deep learning techniques for EHR-based predictions, providing insights into various architectures and their performance.

Diabetes Prediction

This is about type 2 Diabetes, as per Meng et al. (2016) employed logistic regression as well as neural networks on the basis of electronic health records (EHR) in order to predict the onset of type 2 diabetes. The models shown in the paper showed that early prediction is very much on the basis high accuracy, and if the models are trained and tested properly with high accuracy and it is possible to detect type 2 diabetes very easily.

Rationale for Study:

1.Early Detection and Improved Outcomes: Early diagnosing of different diseases such as cancer, diabetes, and cardiovascular conditions can help in improving the outcomes of patients significantly by giving then proper and on a timely manner treatment. It also helps in creating the models that can help in checking the high risk for individuals for the clinical symptoms and also help in reducing the healthcare cost.

2.Integration of Diverse Data Sources: there is a growing availability of the health records which are electronic so it is good to provide the information on the basis of this rich data and predict the models on the basis of real and available data.

3.Addressing Healthcare Challenges: it also helps in addressing the healthcare challenge like cost, volume of patients are also increasing, and there is a huge shortage of healthcare professionals.

4.Technological Advancements: technology is changing every day, there are various algorithms that are coming and training the dataset and also every time new technology came, it is better than the previous one, also resolving previous technology limitations. So this, way technology can be used in order to diagnose the real problem and issues related to human beings.

References:

Meng, X., Zhang, Y., Li, Z., Cheng, L., & Ji, Y. (2016). Predicting the onset of type 2 diabetes mellitus using logistic regression and neural network models.Journal of Biomedical Informatics, 60, 1-7. doi:10.1016/j.jbi.2016.01.004 .Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence.Nature Medicine, 25(1), 44-56. doi:10.1038/s41591-018-0300-7 .

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks.Nature, 542(7639), 115-118. doi:10.1038/nature21056 .

Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., & Dean, J. (2018). Scalable and accurate deep learning with electronic health records.npj Digital Medicine, 1(1), 18. doi:10.1038/s41746-018-0029-1 .

9. Detail study design and methods and justify them if possible

Data collection

Data pre-processing

Feature selection

Model development with the help of different learning models like logistic regression, random forest, and various other algorithms such as CNN, RNN & others.

Model evaluation

Validation

10. Detail a proposed time schedule for the project, with key dates and the milestones of each phase of the project

Total Duration: 4 months

Month 1: Project Planning and Initial Setup

Finalize project proposal and objectives.

Gather and review relevant literature.

Milestone: Project proposal approval and literature review completion.

Month 2: Data Collection and Pre-processing

Feature Selection and Model Development

Identify and acquire public datasets (Breast Cancer Wisconsin, Pima Indians Diabetes, MIMIC-III).

Perform initial data cleaning and pre-processing.

Handle missing values, normalize data, and conduct exploratory data analysis.

Document data pre-processing steps.

Apply feature selection techniques (RFE, PCA).

Finalize feature set for modelling.

Develop baseline machine learning models (logistic regression, decision trees).

: Begin developing advanced models (random forests, neural networks).

Milestone: Completion of data collection and pre-processing and Feature selection and initial model development.

Month 3: Model Training and Evaluation

Model Validation and Sensitivity Analysis

Train models using cross-validation.

Evaluate models using performance metrics (accuracy, precision, recall, F1-score, AUC-ROC).

Refine and optimize models based on evaluation results.

Document model development and evaluation process.

Validate the final model on an independent test dataset.

Conduct sensitivity and robustness analysis.

Iterate on model improvements based on validation results.

Prepare detailed report on model validation and sensitivity analysis

Milestone: Completion of model training and initial evaluation and Final model validation and robustness testing

Month 4: Integration and Finalization

Integrate the predictive model into a user-friendly application or interface (if applicable).

Conduct usability testing and gather feedback.

Make final adjustments based on user feedback.

Milestone: Integration and usability testing completion.

Month 4: Documentation and Presentation

Compile all documentation, including methodology, results, and conclusions.

Prepare final project report and presentation.

Present findings to supervisors, peers, and stakeholders.

Milestone: Submission of final report and project presentation.