Enhancing Diabetic Recovery within the NHS: The Role of Machine Learning in Healthcare Project Scope This project aims to utilize machine learning (ML) algorithms to analyze NHS diabetes data to improve patient outcomes. The scope includes identifying key predictors of recovery, stratifying patients by risk, and optimizing individual treatment plans. Insights from ML models will guide personalized care, resource allocation, and intervention strategies. Research Question How can machine learning algorithms applied to NHS diabetes data enhance patient recovery outcomes and streamline healthcare service delivery? Objectives 1. Predictive Analysis of Recovery Factors: To employ ML algorithms to identify the most significant predictors of successful diabetes recovery within NHS patients. 2. Risk Stratification for Targeted Care: To develop ML models that classify patients into risk categories to better allocate resources and tailor care interventions. 3. Personalized Treatment Optimization: To create a decision-support system using ML models that recommend patient-specific treatment adjustments. Relevance of the Project This project is particularly relevant due to the rising prevalence of diabetes and the strain it places on healthcare systems like the NHS. ML offers a way to analyze complex datasets to improve care quality, efficiency, and patient outcomes, aligning with the NHS's commitment to innovation and patient-centered care. Overview of Methodology 1. Data Preparation: Cleanse and preprocess NHS diabetes data, handling missing values, outliers, and normalization. 2. Exploratory Data Analysis (EDA): Conduct EDA to understand the distributions, correlations, and patterns within the data. 3. Feature Engineering: Derive new features that could be significant predictors of outcomes based on existing data. 4. Model Development: ➤ Predictive Modeling for Patient Outcomes: • Regression Analysis: Predict HbA1c levels, which are critical for managing diabetes, based on the various care processes (e.g., blood pressure checks, cholesterol checks). Random Forest or Gradient Boosting Machines (GBM): Predict the likelihood of patients reaching treatment targets for blood pressure, cholesterol, and other relevant metrics. ➤ Risk Stratification: • Classification Models: Use algorithms like logistic regression, support vector machines, or neural networks to classify patients based on their risk of complications or hospital readmission. ➤ Treatment Optimization: • Reinforcement Learning: Develop a model to recommend personalized treatment adjustments (like insulin dosage) in real time. ➤ Resource Allocation: • Clustering Techniques: Segment patient population by needs to identify groups that might benefit from additional resources or different approaches to care. 5. Model Evaluation and Selection: • • Evaluate models using appropriate metrics (e.g., AUC-ROC for classification, RMSE for regression). Select the best-performing model for each objective. 6. Implementation: • Implement the models to run on new or real-time data within the NHS infrastructure. Develop a user interface for healthcare providers to interact with the ML models. 7. Monitoring and Feedback Loop: • Continuously monitor the model's performance, incorporating feedback from healthcare providers. • Iterate and update models based on new data and insights. References: https://pubmed.ncbi.nlm.nih.gov/37749579/ https://www.researchgate.net/publication/357199337 Machine_learning_and_deep_learning_ predictive_models_for_type 2 diabetes a systematic_review https://www.sciencedirect.com/science/article/pii/S1877050920308024 https://link.springer.com/article/10.1007/s11042-023-16407-5 https://www.nature.com/articles/s41598-020-68771-z https://www.mdpi.com/2075-4418/13/14/2383 https://ouci.dntb.gov.ua/en/works/7BNdGrD7/ https://www.researchgate.net/publication/366606050_Recent_applications_of_machine_learn ing_and_deep_learning_models_in_the_prediction_diagnosis_and_management_of_diabetes _a_comprehensive review/n MSc Business Analysis & Consulting PROJECT GUIDELINES 1-10- University of Strathclyde Business School Introduction The project forms a major part of the course. It tests ability to organise, carry out and report on a significant piece of work. The dissertation should demonstrate both a significant contribution to the problem studied and a grasp of the process of carrying out an effective piece of analysis. Responsibilities and interim deliverables vary from one project to another, as the requirements and structure of each project is unique. The project is assessed independently of other work: the award of MSc is dependent on completion of a satisfactory project and dissertation, however well the candidate may have performed on the rest of the course. This document sets out the main points to be borne in mind, both about the organisation of the project itself and about production of the dissertation. 1: PROJECT ORGANISATION 1.1: Student, Supervisor and Client Many projects are carried out for an external organisation, and students will normally be reporting to an individual client. It is thus important to maintain a good working relationship between the three parties: student, client and University supervisor. Usually the student will have closer day-to-day contact with the client than with his or her supervisor, sometimes for obvious reasons of geography. Nevertheless, students must discuss their progress with their supervisors at mutually agreed intervals, and notify their supervisor (or programme directors if supervisor is not available) immediately in case of any significant problems in their progress (such as substantial delays caused by the client, or personal circumstances). It is not up to supervisors to "chase" students who fail to do so, and the project mark may suffer as a consequence. If for any reason, the student cannot get in touch with their supervisors for more than 2 weeks, they should get in touch with their MSc directors. Students are expected to show independence and initiative, reflecting the fact that they are on the verge of obtaining what amounts to a professional qualification. They should not expect help from their supervisors with every problem encountered. Supervisors will, however, be much more sympathetic to problems that persist despite genuine effort by the student. Students would normally be expected to arrange 4 meetings with their supervisors during the course of their project. It is expected that those meetings will include; an initial meeting at the beginning of the project, two meetings while the project is underway and a final meeting so that the supervisor can provide feedback on a draft of the dissertation. Assuming students provide their supervisor with adequate time to read over draft chapters from their dissertation, supervisors will provide feedback on each chapter once (feedback may be provided on individual chapters OR on a complete draft of the dissertation). “Adequate time" typically means at least two weeks before the submission deadline. Preferences will vary from one supervisor to another and students are encouraged to discuss draft submission deadline with their supervisor. It should be noted that feedback will be sought from the client at the end of the project regarding the student's performance. The responsibilities of each party is summarized below for reference. Student: Initiating, structuring and progressing with the project with his/her own leadership, making sure that the project objectives are met by the given deadline (as well as any interim deliverables if applicable), while considering appropriate contingency plans. Keeping contact with both the client and the supervisor in the areas related to each. Supervisor: Offering general advice on the project and answering methodological questions where needed. As for technical aspects of the work, where needed, directing the student to appropriate sources. Offering advice where there is an obstacle in the progress of the project and getting in touch with the client if needed to resolve this. Warning the student if progress does not seem to be satisfactory. Supervisors typically don't provide feedback for reflections. Guidance can be provided upon request. Client: Defining the project with the help of the student (who is benefiting from the supervisor's advice). Providing required access and contacts to the student. Providing the student with required data and information related to the client company. Answering student's inquiries with regard to questions pertaining to deliverables (expectations) in the project. Resolving any obstacles in the way of progressing with project that is sourced in the client's organisation. MSc director: If for any reason, the student cannot get in touch with their supervisors for more than 2 weeks, they should get in touch with their MSc directors. 1.2: On-Campus and Off-Campus Attendance: Tier 4 students are required to keep in contact with the department office every 2 weeks so we can confirm they are engaged with the University. They are also not allowed to leave the country during the project period with no prior permission from their supervisors and course director, if you do plan to leave the country the dates must be communicated to the department office. All other students must also update their supervisors and the course director if they are planning to take a holiday or absence during the project period or if they are not able to commit themselves to going to client site when required. Failing to do so may result in cancelling and/or failing the dissertation project. 1.3: Project Plan At the start of the project period, the student must consult with client and supervisor to draw up a project plan. This should set out, on one or two sides of paper, the aims of the project, how these will be pursued, a week-by-week timetable of activity, and contingency plans in case of difficulties or delay (e.g. in obtaining data). The project plan should be agreed with supervisor and client within the first two weeks of the project. Taking the plan seriously helps to avoid aimlessness and "drift". This is not to say that changes of plan should never be made. Clearly, there may be unforeseen opportunities and/or problems to allow for. Nevertheless, significant changes to the plan should only be made with the approval of supervisor and client. Allow sufficient time for writing-up the dissertation within the project plan. The writing task is eased considerably if draft chapters are produced as the project proceeds. Students are strongly recommended to do this. Even so, one should allow at least two weeks to produce the final draft, in time to meet the project deadline. 1.4: Executing Projects While projects vary a good deal, the following general points should be noted You must fit in with the client organisation, as regards standards of punctuality, appearance and behaviour, and any safety rules. A general understanding of how a system operates should be gained before attempting any more detailed and technical investigation. Where possible, seek first-hand knowledge, by means of interviews and visits. Make notes during or immediately after any interview or visit, to consolidate what has been heard, discussed or observed. If uncertainties or sources of possible confusion remain, arrange return visits. Do not expect constantly to be told how to proceed. Always remember that the project tests your ability to carry out a competent piece of work, and the contribution made by Data Analytics. You might find it helpful to keep a project diary, setting out what has been achieved each week. 1.5: Group Projects If the project is arranged on a group basis, the group may submit a single project report to the client. However, each individual student must produce a separate introductory section and a separate reflection (see below). 1.6: Internal Projects All the above points apply to projects carried out within the department, except that the supervisor also, in effect, fulfils the role of "client". 1.7: Ethics and Risk Management Guidance All students must apply for ethical and risk management permission irrespective of the nature of the project. This guidance applies to all "standard" projects carried out by Management Science except those dealing with vulnerable groups (e.g. patients, children, drug users etc) or involving the collection of personal confidential data. If you doubt whether your project is a "standard" one then discuss this with your supervisor (for example, a Management Science student might be part of a larger project that involved carrying out medical tests on humans). If you are dealing with a non-standard project, or one that involves vulnerable groups or the collection of personal data, then you may have to obtain approval from the University Ethics Committee in advance. Otherwise you would gain approval from the Departmental Ethics Committee. In all cases you should complete the approval form given at the end of this document as a first step. Students must ensure that all investigatory procedures involving 'participants' are discussed and agreed with their supervisors beforehand. A participant is someone who provides any kind of input to your project (but not the problem owner), for example, someone you observe, interview or give a questionnaire. Data includes any kind of information they give you, including verbal information. Your work should conform to normal professional practice, the Data Protection Act as well as to University Ethics Policy. Participants in your project should: a) Know о what the project is about, о how their data is to be used, о how the data is stored, о whether their data will be used anonymously, and which organisations are involved with the project. b) Not be placed at risk through participation in the study. c) Be able to contact someone else (e.g. the Head of Department) if they have any issues they would like to raise. d) Be able to withdraw from the study and remove data that has been submitted (where this has not been anonymized). e) All information should be in plain language that can be easily understood by the participant. Normally we would expect: That you produce an information sheet for participants that provides the above information. If possible, you should give this in written form for them to read. If this is not appropriate (e.g. on the telephone) then it should be clearly explained, and you should have a script that you will use. That data collected should be held on the university server on your own password protected account. That information will be used anonymously. However, bear in mind that if you are interviewing a small group of people then it is difficult to maintain anonymity because someone might be recognizable from the opinion that they give. Hence people should be made aware of this possibility. Under some circumstances it is not appropriate to keep information anonymously, for example because the person involved is providing information as an expert. That, when dealing with participants in a workplace, you have been given permission by their management to take part, and that the participants know this. All students carrying out management science projects should gain approval from the departmental ethics committee using the ethics form available on MyPlace, and should also include a) The information sheet or script that you use to inform participants about the project. b) The participant consent form. Standard formats are also available on MyPlace for the information sheet and consent form, but these require you to complete specific information relevant to your project. 2: DISSERTATIONS 2.1: Overall Structure Reflecting the aims of the project itself, the dissertation must consist of the following two self- contained documents. 1. Project context/ scene setting. This introductory document should explain the organisational context, background to project, why it's important, why (if) it involves any interesting theoretical points, who was the immediate client for the work and who was the end user (these may or may not be the same people)?. This is material not typically needed by the client, who will already know the organisation and not care much about the theoretical points. The project plan must be included. 2. Client report. This must be submitted exactly as given to the client (partly because we want to assess this, partly to avoid duplication of effort). It will thus be self contained, with its own summary, original page numbers etc. Writing style etc must be aimed unequivocally at client readership, as specified by the scene setting document. You should remember to use the general data analytics literature, and to discuss the choice of methods relative to the nature of the client (e.g. the client's technical sophistication). 3. Reflections. This document is not normally given to the client, but can be by mutual consent. It must discuss points such as: О the process of problem definition (and whether this changed) ○ ○ O ○ ○ why the issues were defined as they were, managing the relationship with the client and/or end users, how these relationships influenced the course of the project and the nature of the report, lessons learnt about OR/BA methodology, the role of OR/BA in the project, things that could have been done, or done differently, difficulties in execution of the project, observations about the client organisation, etc. Reflections should be continuous, not just retrospective: especially, think about these aspects at the start of the project and report these thoughts. Note that these issues must still be addressed with regard to "internal" projects. Supervisors typically don't provide feedback for reflections. Guidance can be provided upon request. You should remember to use the general OR/BA/Management Science literature, and to discuss the choice of methods relative to the nature of the client (e.g. the client's technical sophistication). Also remember that you need to pass the reflection in order to pass your dissertation. Page limit for this section is 10 pages. Appendices can be placed with document 2, as appropriate. Each document may well also have its