Published on

January 5, 2024

Decision Making

How to Build a Disease Detection System Using ML

Machine Learning (ML) techniques for disease detection, including image recognition, algorithm and dataset analysis, and Deep Learning techniques.
Jon Reilly
Co-Founder, Co-CEO, Akkio
Decision Making

In the immortal words of the Greek physician Hippocrates, “prevention is better than cure.” But what if we could go beyond prevention and wield the power of artificial intelligence to detect diseases in their early stages, enabling timely intervention and, ultimately, saving lives? Are you looking for a machine learning (ML) platform to help with disease detection in your clinic or hospital?

Machine learning, the darling of the tech world, has been adopted across a wide range of industries, from finance to marketing. However, its impact in the health sector cannot be overstated. Many studies have demonstrated the benefits of using ML algorithms to detect diseases in their early stages, bolstering the field of medical diagnostics. For instance, one study showcased the prowess of image recognition technology based on ML, identifying white blood cells with an astonishing 90% accuracy rate.

In this article, we'll delve into the exciting world of disease detection using machine learning. We'll introduce you to Akkio, an ML platform that can analyze medical data, and explore how it can revolutionize the way we approach early diagnosis. Say goodbye to the days of relying solely on lab tests and doctor intuition—welcome to the future of medicine, where ML-enhanced diagnostics stand poised to transform the way we detect and treat illness.

How Does Machine Learning Help with Disease Detection?

Machine learning, the offspring of artificial intelligence and computer science, has become a formidable ally in the fight against disease. It harnesses the power of big data, analyzing vast amounts of information to identify patterns and trends that can lead to early disease detection. 

Health professionals can utilize machine learning to determine whether a patient has a disease by comparing their symptoms, genetic history, demographic information, and more against the data of other patients in the system.

So why is machine learning such a game-changer in disease detection? For starters, it streamlines the diagnostic process, eliminating guesswork and unnecessary testing. With machine learning, patients can undergo tests to confirm the algorithm's findings, rather than wasting time and resources on a slew of irrelevant investigations.

Additionally, the high accuracy of machine learning algorithms, when combined with human intervention, reduces the risk of misdiagnoses or overdiagnoses. This becomes particularly valuable in developing countries, where access to affordable medical tests or a robust healthcare system may be limited.

What Diseases Can Be Detected with Machine Learning?

In theory, machine learning can be employed to detect any disease. However, the accuracy and effectiveness of the detection depend on the quality of the data used and the suitability of the ML model for a particular context.

Research has demonstrated the potential of machine learning in detecting a wide array of diseases, including heart disease, diabetes, kidney disease, breast cancer, Parkinson's Disease, Alzheimer's Disease, and more.

The future of medicine lies in the seamless integration of machine learning and healthcare, empowering doctors with cutting-edge technology to detect diseases early and pave the way for timely, life-saving interventions.

Most Common ML Algorithms for Disease Detection

A wide range of machine learning algorithms can be deployed for disease detection, depending on factors like the complexity of the data, and the need for explainability versus accuracy. Akkio’s predictive AI uses neural architecture search (NAS) to automatically select the best model for any dataset.

Decision Tree

Decision trees are powerful machine learning algorithms that follow a divide-and-conquer approach, where attributes take on various values, resulting in a tree-like structure. Classification trees have leaves representing distinct classes, while branches represent combinations of features that lead to these class labels.

Decision trees can also work with continuous variables, called regression trees. Decision trees are intuitive and easy to interpret, making them a good option for disease diagnosis systems where explanations are important. Decision trees are commonly used for simpler, low-dimensionality data and quick data analysis tasks.

Support Vector Machine

Support Vector Machines (SVM) are popular machine learning algorithms used for classification and regression tasks. SVMs work by finding an optimal hyperplane that separates data into distinct clusters, helping to categorize unlabeled data. SVMs are versatile and can handle complex, high-dimensional data, making them suitable for various disease detection applications.

K-Nearest Neighbor (KNN)

K-Nearest Neighbor (KNN) is a nonparametric classification technique that can also be used for regression analysis. KNN works by determining the class membership of a new data point based on the classes of its nearest neighbors. This method is simple to implement and can be effective in disease detection tasks where the underlying relationships between features and classes are unknown.

Naïve Bayes

The naïve Bayes (NB) classifier is a probabilistic classifier based on Bayesian principles. It estimates the membership probability for each class given a data point and predicts the most likely class based on these probabilities. Naïve Bayes is particularly useful for predicting the likelihood of diseases, such as diabetes, where probabilities can inform treatment decisions.

Logistic Regression

Logistic regression (LR) is a machine learning algorithm used primarily for solving classification problems. It uses a probabilistic framework, where predicted values range from 0 to 1. Logistic regression is an effective method for disease detection when the relationship between features and the target variable can be modeled as a linear combination.


Adaptive Boosting, or AdaBoost, is an ensemble classifier that combines multiple weak classifiers into a single, strong classifier. By giving greater weight to difficult samples and less weight to well-classified samples, AdaBoost can effectively handle both classification and regression tasks. This versatile method can be applied to various disease detection scenarios, where an ensemble of models can provide more accurate predictions.

Deep Learning

Deep learning is a subfield of machine learning that employs multiple layers to extract information from input data. It is based on artificial neural networks (ANNs), particularly convolutional neural networks (CNN), which are often combined with other deep learning models. Deep learning has shown great promise in medical fields, including radiology and pathology, where it can automatically detect diseases from medical images and molecular data.

Convolutional Neural Network (CNN)

Convolutional Neural Networks (CNNs) are a type of artificial neural network specifically designed for image processing tasks. CNNs consist of input, hidden, and output layers, where hidden convolutional layers generate feature maps used by subsequent layers. CNNs are particularly effective for tasks like analyzing X-rays or medical scans, where image analysis is crucial for disease detection.

Build an ML Disease Detection System Using Akkio

Akkio is a cloud-based machine learning platform that allows you to build and deploy machine learning models for various use cases, including disease detection. With its no-code interface, you can create powerful ML models in minutes, making it an excellent option for building detection systems for a wide range of diseases.

In this section, we will explore how to use Akkio for machine learning in a disease detection context.

How to use Akkio for Disease Detection

Akkio makes disease detection modeling easy. There’s no need to use Python or manually build deep learning models: All our machine learning methods are no-code, so you can simply connect input data and let the platform do the rest.

Step 1: Data collection

The first step in building an ML disease detection system using Akkio is data collection. For example, you might use a medical dataset like this. This Kaggle dataset contains information related to heart disease, including chest pain, blood pressure, cholesterol levels, and EKG data. Kaggle has countless other datasets, including anonymized or generated medical images that can be used for computer vision tasks, or combined with data mining.

Akkio integrates with popular data collection tools, making it easy to import your data.

Step 2: Data preparation

Data preprocessing also involves handling missing values, removing outliers, and dealing with any potential issues that may lead to overfitting – a situation where the machine learning model performs well on the training data but poorly on new, unseen data. Techniques such as regularization can be applied to reduce overfitting and improve the generalizability of the model.

In this stage, annotated data is used to label specific features or variables that will be employed in the machine learning process. Annotations provide guidance for the model during training, ensuring that it can accurately identify relevant patterns.

Step 3: Feature selection

Next, you need to identify the relevant features or variables that will be used to train the model. This process, known as feature extraction, involves selecting features based on their relevance to the disease being detected. During this stage, activation functions and neurons are utilized to help identify and prioritize the most important features in the dataset.

Machine learning approaches, such as principal component analysis (PCA) and recursive feature elimination (RFE), can be applied to reduce the number of features without losing significant information. 

Step 4: Model training

With the data prepared and features selected, you can now train the machine learning model using Akkio. The platform uses a variety of deep learning algorithms, including random forest, gradient boosting, and deep convolutional neural networks. These models leverage deep learning techniques and optimization methodologies for improved performance in tasks such as image classification, segmentation, and disease classification.

Step 5: Model evaluation

After the prediction model has been trained, it is essential to evaluate its performance using validation data. Metrics such as the confusion matrix and area under the curve (AUC) can be employed to assess the accuracy and generalization of the model.

AUC is also a powerful visualization, as you can see the curve of the true positive rate against the false positive rate. This evaluation allows you to gauge the effectiveness of the machine learning techniques employed in the disease detection system.

Step 6: Model deployment

Once you are satisfied with the model's performance, you can deploy it in a production environment to begin detecting diseases. Akkio provides an API that you can use to integrate the model into your existing systems, making it easy to leverage the power of trained models for early detection and improved patient outcomes.

Below, you can see an example of a disease prediction model made with Akkio. Simply plug in some sample values to make a prediction.

Beyond disease prediction, healthcare practitioners use ML for a number of use-cases, as we can see in countless open access papers. For example, one IEEE paper by Li Liu, Hui Zhang, et. al. discusses the use of deep learning for more accurate medical label barcode identification. Further, a Springer paper by Prasanta Kumar Parida et. al. discusses the use of AI for healthcare informatics, context aware decision making, and more.

Create an ML Disease Detection System with Akkio

Machine learning algorithms have revolutionized the field of disease detection, offering the potential to significantly improve patient outcomes by enabling early diagnosis, accurate predictions, and personalized treatment plans. Health clinics, hospitals, and other healthcare organizations can greatly benefit from integrating ML into their workflow, as it can optimize processes, reduce the burden on medical professionals, and ultimately enhance patient care.

Akkio makes it incredibly easy to create a disease detection system using its predictive AI platform. The user-friendly interface allows users with no prior coding or machine learning experience to build and deploy powerful machine learning models in just a few simple steps. By leveraging Akkio's platform, healthcare organizations can quickly implement ML-powered disease detection systems to support their ongoing efforts in improving patient care.

Akkio offers flexible pricing plans tailored to meet the needs of various organizations and use cases. With a range of options, including a free trial, healthcare organizations can choose the plan that best suits their requirements and budget. By investing in Akkio's platform, organizations can unlock the potential of machine learning to transform their disease detection capabilities and drive better health outcomes for their patients.

Don't miss out on the opportunity to harness the power of machine learning for disease detection. Try Akkio today and discover how easy it is to create a custom disease detection system that can revolutionize your healthcare practice.

By clicking “Accept”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Privacy Policy for more information.