Prediction of Medical Conditions Using Machine Learning Approaches: Alzheimer’s Case Study
Abstract
:1. Introduction
Alzheimer’s Disease (AD) Overview
2. Relevant Resources
2.1. OASIS Datasets
2.2. ADNI Datasets
2.3. The Alzheimer’s Project
- The youngest participant in the dataset is aged 55—therefore, the models can not be used to provide early diagnosis.
- The dataset has a large number of male participants, white and married - therefore, the models may not generalize well to the larger population.
- The methodology should be extended to datasets involving younger patients.
- It should be tested whether the developed models predict well also on under-represented groups in the ADNI dataset.
3. Current Research Directions in Biomarkers Identification
3.1. Neuropsychological Tests
Name | Evaluated Skills | Score Range | Score Interpretation | References |
---|---|---|---|---|
Mini-Mental State Examination (MMSE) | Orientation, Attention, Memory, Language, Visual-Spatial Skills | 0–30 | The greater the impairment, the lower the score | [73,74,75,76,77,78] |
Clinical Dementia Rating Scale (CDRS) | Memory, Orientation, Judgment, Problem Solving, Community Affairs, Home and Hobbies Performance | 0–3 | The greater the impairment, the greater the score | [79,80,81,82,83,84] |
Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS-Cog) | Memory, Orientation, Language, Praxis | 0–70 | The greater the impairment, the greater the score | [85,86,87,88,89] |
Functional Activities Questionnaire (FAQ) | Everyday Functional Abilities | 0–30 | The greater the impairment, the greater the score | [90,91,92,93,94] |
Everyday Cognition (ECog) | Everyday Functional Abilities | 1–4 | The greater the impairment, the greater the score | [95,96,97,98,99,100] |
F-A-S Letter Verbal Fluency (LVF) | Verbal Fluency | Depends on the number of words created | The greater the impairment, the lower the score | [101] |
Logical Memory subtest of the Wechsler Memory Scale | Memory Functions | Depends on the evaluated index (e.g., Visual Memory, Auditory Memory) | The greater the impairment, the lower the score | [102,103,104,105,106,107] |
Delayed total recall (LDELTOTAL) | Ability to recollect information acquired earlier | Depends on the recalled amount of information | The greater the impairment, the lower the score | [72,108,109] |
Modified Preclinical Alzheimer Cognitive Composite with Digit test (mPACCdigit) | Memory Functions | Depends on the recalled amount of information | The greater the impairment, the lower the score | [72,109] |
Modified Preclinical Alzheimer Cognitive Composite with Trails test (mPACCtrailsB) | Processing Speed | - | The greater the impairment, the lower the score | [72,109] |
3.2. Neuroimaging Biomarkers
3.3. Genome, Blood and Cerebrospinal Fluid Biomarkers
3.3.1. CSF Biomarkers
3.3.2. Genome Biomarkers
3.4. Potential Novel Biomarkers
4. Predicting Progression from MCI to AD with Machine Learning Approaches
5. Conclusions and Outlook
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AD | Alzheimer’s Disease |
ADAS-Cog | Alzheimer’s Disease Assessment Scale–Cognitive Subscale |
ADIMO | Alzheimer’s Disease In My Opinion |
ADNI | Alzheimer’s Disease Neuroimaging Initiative |
AI | Artificial Intelligence |
ANN | Artificial Neural Network |
ARDS | Acute Respiratory Distress Syndrome |
CDR | Clinical Dementia Rating |
CDRSB | Clinical Dementia Rating Scale - Sum of Boxes |
CNN | Convolutional Neural Networks |
CNS | Central Nervous System |
CSF | Cerebrospinal Fluid |
CSFOP | Cerebrospinal Fluid Original Poster |
DL | Deep Learning |
ECog | Everyday Cognition |
EL | Ensemble Learning |
EMCI | Early Mild Cognitive Impairment |
EOAD | Early Onset Alzheimer’s Disease |
FAQ | Functional Activities Questionnaire |
FDA | Food and Drug Administration |
GEO | Gene Expression Omnibus |
HC | Healthy Cognition |
HD | Huntington’s Disease |
IADL | Lawton Instrumental Activities of Daily Living Scale |
IXI | Information eXtraction from Images |
LDELTOTAL | Delayed total recall |
LMCI | Late Mild Cognitive Impairment |
LVF | Letter Verbal Fluency |
MCI | Mild Cognitive Impairment |
ML | Machine Learning |
MLP | Multilayer Perceptron |
MMP10 | Matrix metalloproteinases |
MMSE | Mini-Mental State Examination |
mPACCdigit | Modified Preclinical Alzheimer Cognitive Composite with Digit test |
mPACCtrailsB | Modified Preclinical Alzheimer Cognitive Composite with Trails test |
MRI | Magnetic Resonance Imaging |
NPI | Neuropsychiatric Inventory |
OND | Other Neurological Diseases |
PD | Parkinson’s Disease |
PEA | Proximity Extension Assay |
PET | Positron Emission Tomography |
PNS | Peripheral Nervous System |
RFE | Recursive Feature Elimination |
ROI | Region of Interest |
SCG | Scaled Conjugate Gradient |
SVF | Stationary Velocity Field |
WMS | Logical Memory subtest of the Wechsler Memory Scale |
WMS-IV | WMS – fourth edition |
SVM | Support Vector Machine |
RF | Random Forest |
GB | Gradient Boosting |
MTA | medial temporal atrophy |
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Name | Dataset Type | Subjects Count | Scans Count per Patient | AD Subjects Count | Age & Gender for CN | Age & Gender for AD | References |
---|---|---|---|---|---|---|---|
OASIS-1 | Cross-Sectional | 416 | 3 or 4 | 100 | 18–96 years, 119 male, 197 female | 60–96 years, 41 male, 59 female | [42] |
OASIS-2 | Longitudinal (two or more visits) | 150 | 3 or 4 | 64 at initial visits and 14 at later visits | 60–96 years, 22 male, 50 female | 60–96 years, 36 male, 28 female | [43] |
OASIS-3 | Longitudinal (two or more visits) | >1000 | not specified | 489 | 42.5–95.6 years, 358 male, 487 female | 42.5–95.6 years, 254 male, 248 female | [44] |
Data Type | Subcategories | References |
---|---|---|
Clinical Data | Recruitment Data, Demographics, Physical Examinations, Cognitive Assessment Data | [46,47,48,49,50,51,52,53] |
Genetic Data | Genotyping and Sequencing Data | [51,54,55,56,57] |
Medical Images | MRI and Positron Emission Tomography (PET) images | [48,51,58,59,60,61,62,63,64,65,66] |
Biospecimen | Urine, Plasma and Serum from Blood, CSF | [48,61,67] |
American Indian or Alaskan | Asian | Hawaiian | Black or African American | White | Multiple Reported | Not Reported | Total Subjects | |
---|---|---|---|---|---|---|---|---|
Male | 0 | 9 | 0 | 16 | 450 | 2 | 1 | 478 |
Female | 1 | 5 | 0 | 23 | 314 | 1 | 0 | 344 |
TOTAL | 1 | 14 | 0 | 39 | 764 | 3 | 1 | 822 |
Age Group | Enrolled Subjects |
---|---|
Less than 55 | 4 |
55–60 | 22 |
61–65 | 56 |
66–70 | 85 |
71–75 | 244 |
76–80 | 229 |
81–85 | 137 |
86–90 | 44 |
91–95 | 1 |
TOTAL | 822 |
Normal | MCI | AD | Total | |
---|---|---|---|---|
Count | 229 | 405 | 188 | 822 |
Percent | 28% | 49% | 23% | 100% |
Model Type | Model Name | Features | Prediction Type | Training Accuracy Best Model | Test Accuracy Best Model | Train/Test Split |
---|---|---|---|---|---|---|
Cross-Sectional | Logistic Regression | All Baseline Features | Baseline Diagnosis Prediction | 80.6% | 78.3% | 75%/25% |
Cross-Sectional | Logistic Regression | MMSE, CDRSB scores | Baseline Diagnosis Prediction | 93.4% | 92.6% | 75%/25% |
Longitudinal | Logistic Regression | All Baseline Features & Time until last visit | Progression from Cognitively Normal to MCI/AD | 75% | 63% | 80%/20% |
Longitudinal | Logistic Regression | All Baseline Features & Time until last visit | Progression from MCI to AD | 76% | 63% | 80%/20% |
Authors | Dataset Name | Data Type | Preprocessing | Classifier | Classification Accuracy |
---|---|---|---|---|---|
Raut et al., 2017 [114] | OASIS | MRI Data | Contrast enhancement | ANN | 86.8% |
Islam et al., 2018 [115] | OASIS | MRI Data | Image normalization | Ensemble of CNNs | 93.18% |
Bae et al., 2020 [116] | ADNI | MRI Data | Grayscale coronal slices were triplicated into 3 channels | Inception-v4 | 89% |
Sarraf et al., 2017 [118] | ADNI | MRI Data | Removal of non-brain tissues from scans, Image Segmentation, Image Registration using Linear Affine Transformation | GoogleNet | 98.84% |
Pan et al., 2020 [119] | ADNI | MRI Data | Skull Extraction, Registration, Image Smoothing, Voxel-Based MRI Signal Intensity Normalization | Multistage classifier based on CNN | 84.5% |
Biomarker Category | Benefits | Limitations | Best Classifier References | Best Classifier | Best Classifier Dataset | Best Classifier Accuracy |
---|---|---|---|---|---|---|
Cognitive | Easy to conduct, Less expensive, Widely available, Noninvasive, Lack of pain | There is no single test which can indicate a diagnostic | [52] | MLP for AD vs. CN classification | ADNI | 99.76% |
MRI Data | Less expensive, Widely available, Noninvasive, Lack of pain | Decreased hippocampal volume is not AD-specific, Automatic segmentation of scans is challenging, Need expensive infrastructure | [118] | GoogleNet for AD vs. CN classification | ADNI | 98.84% |
CSF | Advanced standardization, High diagnostic performance | Expensive, Invasive | [124] | J48 | Kaggle | 98.82% |
Genetic Data | Can help assess the risk of developing AD | Difficult to collect | [134,135] | MLP/ElasticNet | GEO | 100% |
Authors | Dataset Name | Data Type | Preprocessing | Classifier | Classification Accuracy |
---|---|---|---|---|---|
Sun et al., 2017 [140] | ADNI | MRI Data | ADNI own preprocessings | SVM | 92% |
Peixin et al., 2022 [142] | ADNI | MRI Data | Bias Field Correction, Affline Linear Alignment onto MIN152 atlas, Skull Stripping | Med3D + MoCo | 82% |
Gao et al. [145] | ADNI & IXI | MRI Data | Rigid Registration to MNI152 atlas | 3D CNN + Transfer Learning | 83% for age 75–90, 79% for age 55–75 |
Abrol et al. [146] | ADNI | MRI Data | Segmentation, Spatial Normalization, Gaussian Smoothing | ResNet | 82.7% |
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Stoleru, G.I.; Iftene, A. Prediction of Medical Conditions Using Machine Learning Approaches: Alzheimer’s Case Study. Mathematics 2022, 10, 1767. https://doi.org/10.3390/math10101767
Stoleru GI, Iftene A. Prediction of Medical Conditions Using Machine Learning Approaches: Alzheimer’s Case Study. Mathematics. 2022; 10(10):1767. https://doi.org/10.3390/math10101767
Chicago/Turabian StyleStoleru, Georgiana Ingrid, and Adrian Iftene. 2022. "Prediction of Medical Conditions Using Machine Learning Approaches: Alzheimer’s Case Study" Mathematics 10, no. 10: 1767. https://doi.org/10.3390/math10101767