Machine Learning Model Construction and Testing: Anticipating Cancer Incidence and Mortality
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Source and Selection
2.2. Training and Testing: Division of Data for Model Evaluation
3. Results
3.1. Influential Factors in Cancer Incidence
3.2. Advancements in ML Methods and Prediction Accuracy
4. Discussion
4.1. Consistent Findings: Aligning with Previous Studies on Aging and Cancer Rates
4.2. Revealing Menacing Cancer Mortality Rates
4.3. Promising Direction: ML Models for Cancer Incidence and Mortality Prediction
4.4. Comparative Study of Prediction Models
5. Conclusions
Supplementary Materials
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Breast Cancer Prediction Model | ML Model | |
---|---|---|
Rationales | Use of mathematic formula to predict the cancer | Prediction of cancer via the ML algorithm |
Methods | Use of data to build logic links, connecting factors (e.g., age, height, BMI) and cancer | Prediction via the “black box” without considering the logic links |
Accuracy of prediction | Assumptions and connections | Quality and quantity of data |
Advantages | Matured methods with clear process | Convenient and fact prediction |
Limitations | Incorrect assumption and researchers’ bias | “Black swan” effect |
Reference | [6,7] | [8,9,10,11,12,13] |
Age | Approximate Number | Site | Assigned Number | Year | Assigned Number |
≤1 | 1 | Testis | 1 | 2019 | 1 |
1–4 | 3 | Hodgkin Lymphoma | 2 | 2018 | 2 |
5–9 | 7 | Thyroid | 3 | 2017 | 3 |
10–14 | 12 | Mesothelioma | 4 | 2016 | 4 |
15–19 | 17 | Cervix | 5 | 2015 | 5 |
20–24 | 22 | Brain and Other Nervous System | 6 | 2014 | 6 |
25–29 | 27 | Larynx | 7 | 2013 | 7 |
30–34 | 32 | Melanomas of the Skin | 8 | 2012 | 8 |
35–39 | 37 | Oral Cavity and Pharynx | 9 | 2011 | 9 |
40–44 | 42 | Kidney and Renal Pelvis | 10 | 2010 | 10 |
45–49 | 47 | Leukemias | 11 | 2009 | 11 |
50–54 | 52 | Esophagus | 12 | 2008 | 12 |
55–59 | 57 | Corpus and Uterus, NOS | 13 | 2007 | 13 |
60–64 | 62 | Myeloma | 14 | 2006 | 14 |
65–69 | 67 | Ovary | 15 | 2005 | 15 |
70–74 | 72 | Non-Hodgkin Lymphoma | 16 | 2004 | 16 |
75–79 | 77 | Stomach | 17 | 2003 | 17 |
80–84 | 82 | Urinary Bladder | 18 | 2002 | 18 |
85+ | 87 | Liver and Intrahepatic Bile Duct | 19 | 2001 | 19 |
Pancreas | 20 | 2000 | 20 | ||
Female Breast | 21 | 1999 | 21 | ||
Colon and Rectum | 22 | ||||
Lung and Bronchus | 23 | ||||
Prostate | 24 | ||||
Gender | Assigned Number | Race | Approximate Number | Event Type | Assigned Number |
Female | 1 | Non-Hispanic White | 1 | Incidence | 1 |
Male | 2 | Non-Hispanic Asian/Pacific Islander | 2 | Mortality | 2 |
Non-Hispanic American Indian/Alaska Native | 3 | ||||
Non-Hispanic Black | 4 | ||||
Hispanic of any race | 5 | ||||
CIlower/CIupper | Approximate Number | Incidence/Mortality Rate | Assigned Number | ||
[0–0.5) | 0 | [0–5) | 0 | ||
[0.5–1.5) | 1 | [5–15) | 10 | ||
[1.5–2.5) | 2 | [15–25) | 20 | ||
[2.5–3.5) | 3 | [25–35) | 30 | ||
… | … | … | … |
Incidence rate prediction | Method | Principle | Testing Accuracy | Precision |
Decision tree | Decision-making; nodes are features, leaves are classes | 57.53% | 58.02% | |
Random forest | Ensemble of trees; each trained on random data subsets | 57.90% | 57.31% | |
Logistic regression | Linear model for binary classification | 50.11% | 45.72% | |
SVC | Finding optimal hyperplane; maximizing margin | 49.99% | 46.83% | |
Neural network | Brain-inspired models with layered neurons | 58.92% | 58.21% | |
Mortality rate prediction | Method | Principle | Testing Accuracy | Precision |
Decision tree | Decision-making; nodes are features, leaves are classes | 62.17% | 59.18% | |
Random forest | Ensemble of trees; each trained on random data subsets | 61.92% | 57.71% | |
Logistic regression | Linear model for binary classification | 54.53% | 48.36% | |
SVC | Finding optimal hyperplane; maximizing margin | 55.72% | 50.89% | |
Neural network | Brain-inspired models with layered neurons | 62.30% | 59.32% |
Sites | Reported Incidence Rate per 100,000 | Reported Death Rate per 100,000 |
---|---|---|
Testis | 6.109 | 0.416 |
Hodgkin Lymphoma | 3.357 | 1.052 |
Thyroid | 15.838 | 2.675 |
Mesothelioma | 5.771 | 5.176 |
Cervix | 14.571 | 6.23 |
Brain and Other Nervous System | 7.758 | 7.599 |
Larynx | 14.946 | 7.642 |
Melanomas of the Skin | 26.591 | 8.439 |
Oral Cavity and Pharynx | 21.866 | 9.773 |
Kidney and Renal Pelvis | 34.078 | 15.238 |
Leukemias | 20.492 | 15.742 |
Esophagus | 16.881 | 16.278 |
Corpus and Uterus, NOS | 46.417 | 16.556 |
Myeloma | 25.058 | 18.811 |
Ovary | 20.202 | 19.706 |
Non-Hodgkin Lymphoma | 33.855 | 19.849 |
Stomach | 31.179 | 21.067 |
Urinary Bladder | 58.880 | 23.543 |
Liver and Intrahepatic Bile Duct | 31.211 | 28.477 |
Pancreas | 40.685 | 40.167 |
Female Breast | 137.875 | 51.988 |
Colon and Rectum | 113.632 | 55.407 |
Lung and Bronchus | 169.056 | 139.842 |
Prostate | 429.041 | 169.678 |
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Ding, Y. Machine Learning Model Construction and Testing: Anticipating Cancer Incidence and Mortality. Diseases 2024, 12, 139. https://doi.org/10.3390/diseases12070139
Ding Y. Machine Learning Model Construction and Testing: Anticipating Cancer Incidence and Mortality. Diseases. 2024; 12(7):139. https://doi.org/10.3390/diseases12070139
Chicago/Turabian StyleDing, Yuanzhao. 2024. "Machine Learning Model Construction and Testing: Anticipating Cancer Incidence and Mortality" Diseases 12, no. 7: 139. https://doi.org/10.3390/diseases12070139