ProjectProposal_Group141_WI24.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "42a14256-91c8-446e-92a7-ab6bf11055d3",
   "metadata": {},
   "source": [
    "# **COGS 108 - Project Proposal**\n",
    "\n",
    "# **Names**\n",
    "\n",
    "-   Shivangi Gupta\n",
    "\n",
    "-   Joseph Hwang\n",
    "\n",
    "-   Zijun Yang\n",
    "\n",
    "-   Johnny Gonzales\n",
    "\n",
    "-   Tanishq Rathore\n",
    "\n",
    "# **Research Question**\n",
    "\n",
    "Utilizing clinical MRI Data and personal details of an individual, can we predict via machine learning model whether\n",
    "an individual will have an onset of Alzheimer's disease? Features the model will be trained on include variables such as Mini Mental State Examination (MMSE), visit number, Clinical Dementia Rating (CDR), gender, age, years of education, socioeconomic status, Estimated total intracranial volume (eTIV), Normalize Whole Brain Volume (nWBV), and Atlas Scaling Factor (ASF).\n",
    "\n",
    "## **Background and Prior Work**\n",
    "\n",
    "<u><ins>Background<ins></u>\n",
    "\n",
    "Advancements in healthcare, improvements in living conditions, and\n",
    "breakthroughs in medicine have collectively contributed to longer life\n",
    "expectancies worldwide; simultaneously, developed countries are also\n",
    "experiencing declining fertility\n",
    "rates.[<sup>1</sup>](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4255510/)\n",
    "The combination of these two circumstances has resulted in the\n",
    "proportion of older people within populations to steadily increase. The\n",
    "World Health Organization (WHO) reported that “in 2020, the number of\n",
    "people aged 60 and older outnumbered children younger than 5\n",
    "years”.[<sup>2</sup>](https://www.who.int/news-room/fact-sheets/detail/ageing-and-health)\n",
    "In addition, they also state that “between 2015 and 2050, the proportion\n",
    "of the world’s population over 60 years will nearly double from 12% to\n",
    "22%”. As a result, it is reasonable that we examine common health\n",
    "conditions associated with older age, one being Alzheimer’s disease.\n",
    "\n",
    "So what is Alzheimer’s disease? Alzheimer’s disease is a progressive\n",
    "neurodegenerative brain disorder that impairs memory and cognitive\n",
    "functions. It is the most common cause of dementia and affects about 6.5\n",
    "million people in the United States who are aged 65 and\n",
    "older.[<sup>3</sup>](https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/symptoms-causes/syc-20350447)\n",
    "At the moment, there are no cures for the disease but medicines may\n",
    "improve or slow the progression of\n",
    "symptoms.[<sup>3</sup>](https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/symptoms-causes/syc-20350447)\n",
    "As such, it is our project to create a model that is able to predict\n",
    "Alzheimer’s disease based on clinical data that include factors that\n",
    "show risk and progression of the disease.\n",
    "\n",
    "There are several other projects that have asked similar questions and\n",
    "approached similar problems for other diseases. For instance, one study\n",
    "tried to use machine learning methods to predict risk of cardiovascular\n",
    "disease based on major contributing\n",
    "factors.[<sup>4</sup>](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036320/)\n",
    "Similarly, another paper used machine learning and ranker-based feature\n",
    "selection methods to predict eye diseases based on\n",
    "symptoms.[<sup>5</sup>](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854513/)\n",
    "Lastly, there was a paper that predicted thyroid disease using selective\n",
    "features and machine learning\n",
    "techniques.[<sup>6</sup>](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9405591/)\n",
    "All three papers seem to have been relatively successful in predicting\n",
    "the disease based on distinct features. Evidently, training machine\n",
    "learning models based on datasets which contain factors and indicators\n",
    "for a given disease is not a novel format of question and method; we\n",
    "hope to achieve similarly for Alzheimer’s disease.\n",
    "\n",
    "<u><ins>In-Depth Study Analysis<ins></u>\n",
    "\n",
    "Our group analyzed two studies published in the National Institute of\n",
    "Health’s (NIH) journal database. The first study[<sup>7</sup>](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8927715/) employed\n",
    "machine learning models to predict early-stage Alzheimer's Disease using\n",
    "Open Access Series of Imaging Studies (OASIS) data, focusing on metrics\n",
    "like precision, recall, accuracy, and F1-score. The authors, with\n",
    "backgrounds in technology and health research, aimed to enhance early\n",
    "diagnosis, potentially lowering Alzheimer's mortality rates. The study\n",
    "demonstrated that machine learning techniques such as decision trees,\n",
    "random forests, SVM, gradient boosting, and voting classifiers can\n",
    "effectively predict early-stage Alzheimer's Disease with an accuracy of\n",
    "up to 83%. This achievement highlights the critical role of data science\n",
    "in identifying Alzheimer's at an early phase, leveraging feature\n",
    "selection and advanced algorithms to enhance diagnostic accuracy. Early\n",
    "detection is crucial for timely intervention, potentially mitigating the\n",
    "disease's progression and impact on patients and their families (Kavitha\n",
    "et al.).\n",
    "\n",
    "The second study[<sup>8</sup>](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138240/) titled\n",
    "“Application of machine learning methods for diagnosis of dementia based\n",
    "on the 10/66 battery of cognitive function tests in south India”\n",
    "investigated the use of machine learning for diagnosing dementia in\n",
    "South India, employing the culturally and educationally fair 10/66\n",
    "battery of cognitive function tests designed for use in low and\n",
    "middle-income countries. Through the analysis of neuropsychological\n",
    "data, demographic information, and normative data, the research applied\n",
    "Jrip classification algorithm among others, achieving high diagnostic\n",
    "accuracy. This approach demonstrates the potential to streamline the\n",
    "diagnostic process, making it quicker and more accessible for clinicians\n",
    "and patients in India, thereby addressing the significant healthcare\n",
    "challenge of efficiently identifying dementia in community settings\n",
    "(Bhagyashree et al).\n",
    "\n",
    "<u><ins>In-Depth Analysis of Similar Projects<ins></u>\n",
    "\n",
    "Our group also delved into actual Kaggle projects that are directly\n",
    "associated with the dataset we’ve chosen to use, delving into EDA and\n",
    "prediction models using Scikit-Learn and Tensorflow. The first\n",
    "project[<sup>9</sup>](https://www.kaggle.com/code/shreyaspj/alzheimer-s-analysis-using-mri)\n",
    "I will discuss starts with an introduction to Alzheimer's disease and\n",
    "the problem statement of estimating the Clinical Dementia Rating (CDR)\n",
    "using MRI dataset features. It progresses through data loading and\n",
    "preprocessing, including null value handling and normalization, and\n",
    "employs machine learning techniques, specifically mentioning model\n",
    "training with hyperparameter tuning for XGBClassifier and\n",
    "GradientBoostingClassifier. The notebook concludes with predictions and\n",
    "performance evaluation, indicated by confusion matrix and classification\n",
    "report visualizations, and the model was able to reach a final accuracy\n",
    "of \\~80%.\n",
    "\n",
    "The second project[<sup>10</sup>](https://www.kaggle.com/code/andrew32bit/predict-alzheimer-disease-sl-and-tf) tried to predict the Clinical Rating of Alzheimer's disease (CRA) by\n",
    "integrating data loading, visualization, and extensive machine learning,\n",
    "including the use of TensorFlow for neural network models. It explored\n",
    "various machine learning models, with a special emphasis on model\n",
    "training and evaluation, culminating in the finding that the\n",
    "DecisionTreeClassifier performed the best among the models tested. The\n",
    "conclusion stressed the need for more data to enhance the precision of\n",
    "Alzheimer's disease predictions, highlighting the challenge of data\n",
    "scarcity in achieving accurate diagnostic models.\n",
    "\n",
    "**References**\n",
    "\n",
    "1.  [^](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4255510/) Nargund G. (2009) Declining birth rate in Developed Countries: A\n",
    "     radical policy re-think is required. *Facts, views & vision in\n",
    "     ObGyn, 1(3), 191–193.*\n",
    "     [<u>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4255510/</u>](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4255510/)\n",
    "\n",
    "2.  [^](https://www.who.int/news-room/fact-sheets/detail/ageing-and-health) World Health Organization. (1 Oct 2022) Ageing and health. *World\n",
    "     Health Organization*.\n",
    "     [<u>https://www.who.int/news-room/fact-sheets/detail/ageing-and-health</u>](https://www.who.int/news-room/fact-sheets/detail/ageing-and-health)\n",
    "\n",
    "3.  [^](https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/symptoms-causes/syc-20350447) Mayo Foundation for Medical Education and Research. (30\n",
    "     August 2023) Alzheimer’s disease. *Mayo Clinic*.\n",
    "     [<u>https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/symptoms-causes/syc-20350447</u>](https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/symptoms-causes/syc-20350447)\n",
    "\n",
    "4.  [^](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036320/) Peng, M., Hou, F., Cheng, Z., Shen, T., Liu, K., Zhao, C., &\n",
    "     Zheng, W. (23 Mar 2023) Prediction of cardiovascular disease risk\n",
    "     based on major contributing features. *Scientific reports, 13(1),\n",
    "     4778*.\n",
    "     [<u>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036320/</u>](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036320/)\n",
    "\n",
    "5.  [^](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854513/) Marouf, A. A., Mottalib, M. M., Alhajj, R., Rokne, J., &\n",
    "     Jafarullah, O. (24 Dec 2022) An Efficient Approach to Predict Eye\n",
    "     Diseases from Symptoms Using Machine Learning and Ranker-Based\n",
    "     Feature Selection Methods. *Bioengineering (Basel, Switzerland),\n",
    "     10(1), 25*.\n",
    "     [<u>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854513/</u>](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854513/)\n",
    "\n",
    "6.  [^](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9405591/) Chaganti, R., Rustam, F., De La Torre Díez, I., Mazón, J. L. V.,\n",
    "     Rodríguez, C. L., & Ashraf, I. (13 Aug 2022). Thyroid Disease\n",
    "     Prediction Using Selective Features and Machine Learning\n",
    "     Techniques. *Cancers, 14(16), 3914*.\n",
    "     [<u>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9405591/</u>](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9405591/)\n",
    "\n",
    "7.  [^](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8927715/) Kavitha C, Mani V, Srividhya SR, Khalaf OI, Tavera Romero CA.\n",
    "     Early-Stage Alzheimer's Disease Prediction Using Machine Learning\n",
    "     Models. Front Public Health. 2022 Mar 3;10:853294. doi:\n",
    "     10.3389/fpubh.2022.853294. PMID: 35309200; PMCID: PMC8927715.\n",
    "     [<u>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8927715/</u>](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8927715/)\n",
    "\n",
    "8.  [^](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138240/) Bhagyashree SIR, Nagaraj K, Prince M, Fall CHD, Krishna M.\n",
    "     Diagnosis of Dementia by Machine learning methods in\n",
    "     Epidemiological studies: a pilot exploratory study from south\n",
    "     India. Soc Psychiatry Psychiatr Epidemiol. 2018 Jan;53(1):77-86.\n",
    "     doi: 10.1007/s00127-017-1410-0. Epub 2017 Jul 11. PMID: 28698926;\n",
    "     PMCID: PMC6138240.\n",
    "     [<u>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138240/</u>](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138240/)\n",
    "\n",
    "9.  [^](https://www.kaggle.com/code/shreyaspj/alzheimer-s-analysis-using-mri) Reddy, Shreyas. 2021. Alzheimer's analysis using MRI, February\n",
    "     8, 2024.\n",
    "     [<u>https://www.kaggle.com/code/shreyaspj/alzheimer-s-analysis-using-mri</u>](https://www.kaggle.com/code/shreyaspj/alzheimer-s-analysis-using-mri)\n",
    "\n",
    "10. [^](https://www.kaggle.com/code/andrew32bit/predict-alzheimer-disease-sl-and-tf) Andrew. 2017. Predict alzheimer disease sl and tf, February 8, 2024.\n",
    "     [<u>https://www.kaggle.com/code/andrew32bit/predict-alzheimer-disease-sl-and-tf</u>](https://www.kaggle.com/code/andrew32bit/predict-alzheimer-disease-sl-and-tf)\n",
    "\n",
    "# **Hypothesis**\n",
    "\n",
    "Our project's hypothesis is the following: \"It is possible to predict the onset of Alzheimers based on the combination of (1) clinical data* and (2) personal features such as gender, age, years of education, and socioeconomic status.\" We believe that we will be able to successfully train a model that is able to predict the onset of Alzheimer's disease because, as mentioned in the background portion of the proposal, there has been numerous successful machine learning models trained on clinical data to predict the onset of a disease. The clinical data provide variables that capture both the cognitive and structural changes associated with the disease's progression. Incorporating personal features such as gender and age in the prediction model is justified by extensive research indicating that these factors can influence the risk and progression rate of Alzheimer's disease. Together, we believe that this will be enough data to train a model to predict the onset of Alzheimer's disease.\n",
    "\n",
    "*Clinical data include Mini Mental State Examination (MMSE), visit number, Clinical Dementia Rating (CDR), Estimated total intracranial volume (eTIV), Normalize Whole Brain Volume (nWBV), and the Atlas Scaling Factor (ASF)\n",
    "\n",
    "# **Data**\n",
    "\n",
    "1. The ideal dataset to train our machine learning model would contain clinical and personal variables that capture the progression and risk level of Alzheimer's disease. These datasets would have to be found online from reputable medical research centers or through large-scale longitudinal studies focusing on aging and dementia. Within these studies, MRI scans and protocols should be conducted using standardized procedures for consistency across the dataset.\n",
    "\n",
    "     In addition, a large sample size would be ideal, although preliminary searches in clinical data seem to range around 500 for diseases. We also note that although incorporating many variables will make it difficult to interpret and determine what the main driving factor is in which makes the model predict the onset of the disease in an individual, our goal is just to create a model that is able to predict the onset given a set of clinical and personal variables that are commonly available. Perhaps determining which factor influences the model's decision the most may be another interesting avenue to explore.\n",
    "\n",
    "     The clinical variables in which we want to examine are the following:\n",
    "     - Mini Mental State Examination (MMSE): A measure of cognitive impairment.\n",
    "     - Clinical Dementia Rating (CDR): A numerical scale used to quantify the severity of symptoms of dementia.\n",
    "     - Estimated Total Intracranial Volume (eTIV)\n",
    "     - Normalize Whole Brain Volume (nWBV)   \n",
    "     - Atlas Scaling Factor (ASF): Measures of brain volume and structure, obtained from MRI scans, that can indicate brain atrophy associated with Alzheimer's.\n",
    "     - Visit Number\n",
    "\n",
    "     Personal features that are commonly associated with Alzhiemer's risk that we also want to include in our project are the following:\n",
    "     - Gender\n",
    "     - Age\n",
    "     - Years of Education\n",
    "     - Socioeconomic Status\n",
    "     - Indicators of cognitive reserve\n",
    "\n",
    "2. Searching for potential datasets, we found that there is the Open Access Series of Imaging Studies (OASIS) which makes available MRI data sets related to Alzheimer's disease. We were able to download the dataset and found that it included clinical and personal variables that we ideally wanted to incorporate. OASIS makes available these data to be accessible through csv files which we can download and utilize independently. There is also the UK Biobank which is a large scale database with 500,000 participants which include patient information and brain imaging data, but does not have all indicators. Lastly, we found data from the National Alzheimer's Coordinating Center (NACC) which collects and stores data from Alzheimer's research centers around the US. The data appears reliable but is not in a standardized format (which requires cleaning as a result).\n",
    "\n",
    "# **Ethics & Privacy**\n",
    "\n",
    "Ethics & Privacy Considerations:\n",
    "\n",
    "Biases/Privacy/Terms of Use Issues with Proposed Data:\n",
    "\n",
    "1.  The potential datasets considered, such as OASIS MRI, UK Biobank,\n",
    "     and NACC, may have biases and privacy considerations. For\n",
    "     instance, the OASIS MRI project's terms of use and participant\n",
    "     selection could introduce biases. UK Biobank, despite its size,\n",
    "     might not include a diverse representation of certain populations,\n",
    "     leading to potential biases in the dataset.\n",
    "\n",
    "Potential Biases in Dataset Composition and Collection:\n",
    "\n",
    "2.  Biases may arise in dataset composition and collection, affecting\n",
    "     the equitable analysis of Alzheimer's prediction. For example, if\n",
    "     the data predominantly includes participants from specific\n",
    "     demographic groups, it could introduce biases in the model.\n",
    "     Additionally, variations in data collection methods across\n",
    "     different research centers, as in the case of NACC, may impact\n",
    "     standardization, potentially leading to biases.\n",
    "\n",
    "Detection and Mitigation of Biases:\n",
    "\n",
    "3.  To detect biases, the group will conduct a thorough review of the\n",
    "     dataset sources, including participant demographics and\n",
    "     recruitment methods. During data preprocessing, the team will\n",
    "     analyze variables for potential biases, ensuring a balanced\n",
    "     representation. The group plans to collaborate with experts in the\n",
    "     field and seek external input to validate the fairness and\n",
    "     inclusivity of the dataset.\n",
    "\n",
    "Other Issues Related to Privacy and Equitable Impact:\n",
    "\n",
    "4.  Privacy concerns arise from the sensitive nature of medical data,\n",
    "     especially in Alzheimer's research. Ensuring participant anonymity\n",
    "     and adhering to privacy regulations are paramount. Equitable\n",
    "     impact considerations involve understanding if the model's\n",
    "     predictions could disproportionately affect certain groups. It is\n",
    "     essential to communicate findings responsibly, avoiding\n",
    "     reinforcing existing biases or stigmatizing specific populations.\n",
    "\n",
    "Handling Identified Issues:\n",
    "\n",
    "5.  The group commits to transparently communicating any identified\n",
    "     biases throughout the research process. Mitigation strategies will\n",
    "     be implemented during data preprocessing and model development.\n",
    "     The team will consider alternative datasets or additional sampling\n",
    "     methods if biases persist. Ethical review boards will be\n",
    "     consulted, and the group aims to publish findings with a clear\n",
    "     acknowledgment of potential limitations and biases, promoting\n",
    "     responsible and equitable use of the predictive model.\n",
    "\n",
    "In summary, the group is dedicated to addressing ethical concerns\n",
    "comprehensively, from data collection to analysis and post-analysis.\n",
    "Transparency, collaboration with experts, and continuous evaluation of\n",
    "potential biases will guide the research, ensuring responsible and\n",
    "ethical development of the Alzheimer's prediction model.\n",
    "\n",
    "# **Team Expectations**\n",
    "\n",
    "1. Clear communication and relatively reasonable responsiveness to messages sent in our Discord group chat.\n",
    "2. Shared Responsibility and Accountability: finish all tasked work by the designated due date.\n",
    "3. Maintain quality work and attention to detail; ask questions when necessary.\n",
    "4. Attendance and Participation: during designated days in which we meet (either through Zoom or in-person), everyone should be present unless notified previously in advance. \n",
    "\n",
    "\n",
    "# **Project Timeline Proposal**\n",
    "\n",
    "| **Meeting Date** | **Meeting Time** | **Completed Before Meeting**                                     | **Discuss at Meeting**                                                                                |\n",
    "|-----------|-----------|-----------------------------|-----------------------|\n",
    "| 2/10              | 1 PM             | Edit, finalize, and submit proposal; Search for datasets (everyone)         | Discuss Wrangling and possible analytical approaches; Assign group members to lead each specific part |\n",
    "| 2/16             | 6 PM             | Import & Wrangle Data (Johnny); EDA (Shivangi)                   | Review/Edit wrangling/EDA; Discuss Analysis Plan                                                      |\n",
    "| 2/23             | 12 PM            | Finalize wrangling/EDA; Begin Analysis (Joseph; Zijun)           | Discuss/edit Analysis; Complete project check-in                                                      |\n",
    "| 3/16             | 12 PM            | Complete analysis; Draft results/conclusion/discussion (Tanishq); everyone will make/record our presentation | Discuss/edit full project; Record our video                                                                            |\n",
    "| 3/20             | Before 11:59 PM  | Finalize/proofread project conclusion and submit report and video (everyone)                                                               | Turn in Final Project & Group Project Surveys                                                         |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba0dd509",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}