10 Real-World Machine Learning Projects in Healthcare

How Does Artificial Intelligence Work in Healthcare?

Let’s start with determining what tasks AI helps to accomplish in the Healthcare industry.

1. Data Collection: AI can be a great help in aggregating and managing vast amounts of patient data from diverse sources, including Electronic Health Records (EHRs), wearable devices, and other medical records systems. It is even possible to collect voice data and convert it into text with the help of Natural Language Processing. This comprehensive dataset encompasses critical information such as the patient’s medical history, symptoms, lab results, imaging scans, and treatment outcomes. 
2. Preprocessing: At the next stage, the trick is that we can’t feed collected data into the Machine Learning algorithms from the get-go since one additional step is required. The preprocessing step is essential to ensure data quality and compatibility. AI algorithms clean noisy data, normalize values, and perform feature engineering to extract relevant information and create meaningful features that enhance the algorithm’s learning process.
3. Real-time Prediction: Now, when high-quality data is extracted, Machine learning algorithms can operate in real-time, continuously analyzing incoming patient data to make predictions or classifications. For instance, based on a patient’s symptoms and medical history, AI can predict the likelihood of certain diseases or conditions, enabling timely interventions and personalized care plans. The same concept works perfectly for other industries as well, for example, predictive analytics in Retail, which unlocks the power of Demand Forecasting, Customer Segmentation, and Personalized Recommendations for the industry. 
4. Decision Support: Automated predictions may serve as invaluable decision-support Machine Learning tools for healthcare providers. By highlighting high-risk patients who require closer monitoring or recommending personalized treatment plans based on individual patient characteristics, AI assists healthcare professionals in making more informed and tailored clinical decisions. Similarly, decision support can be used in other industries, for example, for ML-enabled demand forecasting in Retail. 
5. Monitoring and Evaluation: Continuous monitoring and evaluation of Machine Learning algorithms are crucial to ensuring their accuracy and effectiveness over time. Healthcare organizations should closely track the algorithm’s performance, identifying any discrepancies or issues promptly to maintain reliability and enhance patient outcomes.
6. Feedback Loop: As new data becomes available and additional outcomes are observed, Machine Learning algorithms should undergo periodic updates and retraining to improve their performance and adapt to evolving circumstances. This iterative feedback loop ensures that the algorithms remain responsive to changing patient needs and emerging healthcare trends.

Serhii Leleko

ML & AI Engineer at SPD Technology

“While working with data in the healthcare industry, it is important to always implement robust data governance policies to maintain the accuracy, completeness, and integrity of patient data. Make sure your organization adheres to strict data security and privacy regulations, such as HIPAA, to safeguard patient confidentiality and comply with legal requirements.”

Machine Learning Algorithms Used In Healthcare

It’s time to move on to discussing the most common algorithms that will most likely be used in Machine Learning applications in Healthcare. If you want to discover the basics, we recommend you start with our introduction to Machine Learning.

Logistic Regression

This algorithm is known for binary classification tasks, where the outcome is either positive or negative. In the Healthcare industry, it is used to determine the disease diagnosis and the presence or absence of a certain condition. It’s extensively used due to its simplicity, interpretability, and effectiveness in modeling probability. One of the other prominent examples of the usage of this algorithm from other industries includes Fraud Detection with Machine Learning.

The tasks this algorithm helps to accomplish include:

• Predicting the likelihood of developing a disease based on risk factors.
• Distinguishing between patients with and without a specific condition or disease.
• Analyzing risk factors and associations with diseases in epidemiological clinical trials.

Decision Trees

Being popular due to their simplicity and interpretability, they’re suitable for both classification and regression tasks and can handle a mix of numerical and categorical data. Decision trees are often preferred in scenarios where explainability of the model’s decisions is crucial, such as in clinical settings where healthcare professionals and patients need to understand the reasoning behind diagnosis or treatment recommendations.

The tasks these algorithms help to accomplish include:

• Assisting in identifying the most likely diagnosis based on patient symptoms and medical history.
• Suggestion of appropriate treatment options based on patient characteristics.
• Identifying patients at high risk of developing complications or adverse outcomes.

Random Forest

It is an ensemble learning method composed of multiple decision trees that are highly robust and accurate. They excel in handling complex datasets with high dimensionality and noisy or missing data. For healthcare organizations, where datasets are often incomplete, random forest is particularly valuable.

The tasks this algorithm helps to accomplish include:

• Predicting the likelihood of disease onset based on patient characteristics and risk factors.
• Predicting patient outcome, the likelihood of recovery or death.
• Feature importance analysis, identifying the most influential variables or risk factors associated with a particular condition.

Support Vector Machines (SVM)

These are powerful classifiers, particularly effective in handling high-dimensional data and nonlinear relationships. They’re widely used by Healthcare organizations for tasks requiring accurate classification, such as disease diagnosis and patient risk stratification.

The tasks these algorithms help to accomplish include:

• Disease classification, for example, selection between different types or stages of cancer patients, based on medical imaging or genomic data.
• Categorizing patients into risk groups based on clinical variables or biomarkers.
• Medical image analysis, for example, segmenting and classifying tumors in radiological images.

Neural Networks

Neural networks, especially Deep Learning algorithms, and Deep Learning models like CNNs and RNNs, are capable of learning complex patterns and relationships from large volumes of data. They’re highly versatile and find applications across various healthcare domains, from medical image analysis to clinical decision support and genomics.

The tasks this algorithm helps to accomplish include:

• Analysis of medical images, like detecting abnormalities in X-rays, MRIs, or histopathological slides.
• Clinical decision support systems for risk prediction, treatment recommendation, and patient monitoring.
• Analysis of time-series data, such as electronic medical records, to predict patient outcomes and detect anomalies.

K-Nearest Neighbors (KNN)

A simple yet effective algorithm for both classification and regression tasks. It’s particularly useful in scenarios with small to medium-sized datasets and can handle numerical and categorical data points effectively. It is quite popular for anomaly detection with Machine Learning tasks, so we recommend you read our article if you want to learn more. 

The tasks this algorithm helps to accomplish include:

• Disease classification tasks, such as predicting the likelihood of a patient having a particular condition based on similar patients in the dataset.
• Patient similarity analysis, identifying individuals with similar medical profiles for personalized treatment plans and recommendations.
• Anomaly detection tasks, identifying outliers in patient vital signs or laboratory results.