{"id":3375,"date":"2023-08-31T10:59:22","date_gmt":"2023-08-31T10:59:22","guid":{"rendered":"https:\/\/neuraldesigner.com\/blog\/cancer\/"},"modified":"2025-09-09T17:27:28","modified_gmt":"2025-09-09T15:27:28","slug":"cancer","status":"publish","type":"blog","link":"https:\/\/www.neuraldesigner.com\/blog\/cancer\/","title":{"rendered":"9 applications of machine learning in cancer"},"content":{"rendered":"

The ability of Machine Learning tools to detect critical features from complex datasets reveals their importance in cancer diagnosis.<\/p>\n

The components of early detection of cancer are early diagnosis and screening.<\/p>\n

Early diagnosis focuses on detecting symptomatic patients as early as possible while screening tests healthy individuals to identify those who have cancer before symptoms appear.<\/p>\n

The cancer type’s early diagnosis and prognosis have become necessary in cancer research. Indeed, it can facilitate the subsequent clinical management of patients.<\/p>\n

Here are some examples of cancer diagnoses solved using Neural Designer<\/a>. Download the free trial<\/a> to follow them step by step.<\/p>\n

 <\/p>\n

\n

Pancreatic cancer diagnosis<\/a><\/h2>\n

<\/a><\/p>\n

This example is about a non-invasive way to diagnose pancreatic cancer, the urinary biomarkers. The most commonly used was plasma, an invasive blood biomarker, a few years back. Combining these two methods improves the model’s accuracy in a pancreatic cancer diagnosis.<\/p>\n

In this example, we divide our primary dataset into four subsets. Each allows us to study whether a patient is healthy, has a benign tumour, or pancreatic cancer stages I, II, III, or IV.
\nRead more.<\/a><\/p>\n<\/section>\n

\n

Lung cancer<\/a><\/h2>\n

<\/a><\/p>\n

The main goal of this study is to build a model that provides a lung cancer risk assessment and decision support tool to facilitate prevention and screening discussions between individuals and their healthcare providers. This task will be satisfied through a questionnaire with different questions about cancer risk factors and pathologies.
\nRead more<\/a><\/p>\n<\/section>\n

\n

Breast cancer diagnosis<\/a><\/h2>\n

<\/a><\/p>\n

This example aims to diagnose whether a lump in a breast could be malignant (cancerous) or benign (non-cancerous).
\nWe use imaging parameters from digitized images of a fine-needle aspiration biopsy to build the classification model.
\nRead more.<\/a><\/p>\n<\/section>\n

\n

Leukemia microarray analysis<\/a><\/h2>\n

<\/a><\/p>\n

This example aims to diagnose leukemia in patients and differentiate between Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML), depending on their DNA coding.
\nRead more.<\/a><\/p>\n<\/section>\n

\n

Colon cancer treatment<\/a><\/h2>\n

<\/a><\/p>\n

This example examines data from an RCT, measuring the effect of a particular drug combination on colon cancer.
\nSpecifically, we are looking at the effect of Levamisole and Fluorouracil on patients who have had surgery to remove their colon cancer. After surgery, the patient’s recovery depends on the remaining residual cancer. This study shows which drug combination had a beneficial effect: Chemotherapy or Levamisole and Fluorouracil.
\nRead more.<\/a><\/p>\n<\/section>\n

\n

Cervical cancer prognosis <\/a><\/h2>\n

<\/a><\/p>\n

This study aims to create a prognostic model based on Artificial Neural Networks that focuses on the initial screening stages.
\nRead more.<\/a><\/p>\n<\/section>\n

\n

Lung cancer recurrence<\/a><\/h2>\n

<\/a><\/p>\n

This example assesses the probability of suffering a relapse in lung cancer patients. We use expression data from 335 patients with eighteen thousand genes and some phenotypic variables.
\nRead more.<\/a><\/p>\n<\/section>\n

\n

Breast cancer mortality prediction<\/a><\/h2>\n

<\/a><\/p>\n

This example predicts the mortality of breast cancer patients over five years. We use different data types, including clinical and treatment variables, an expression, and a mutation panel.
\nRead more.<\/a><\/p>\n<\/section>\n

\n

Colorectal cancer<\/a><\/h2>\n

<\/a><\/p>\n

This example assesses the risk of a patient with colorectal cancer developing liver metastasis. We use mutational data from 492 genes and phenotypic variables using machine learning.
\nRead more.<\/a><\/p>\n<\/section>\n

\n

Related posts<\/h2>\n<\/section>\n","protected":false},"author":1,"featured_media":2500,"template":"","categories":[],"tags":[38],"class_list":["post-3375","blog","type-blog","status-publish","has-post-thumbnail","hentry","tag-healthcare"],"acf":[],"yoast_head":"\n9 applications of machine learning in cancer<\/title>\n<meta name=\"description\" content=\"Machine learning tools have the ability to detect critical features from complex data sets, revealing their importance in cancer diagnosis.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.neuraldesigner.com\/blog\/cancer\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"9 applications of machine learning in cancer\" \/>\n<meta property=\"og:description\" content=\"Machine learning tools have the ability to detect 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