Chronic conditions like diabetes, heart disease, cancer, and chronic respiratory illnesses (like Asthma and COPD) are the primary reasons for death and disability globally.
According to the World Health Organization (WHO), around 71% of deaths worldwide are due to chronic diseases.
That’s right, 71%! If chronic diseases were a character in a movie, they would be the villain that just won’t die, no matter how many sequels are made!
Managing these diseases is intricate, necessitating ongoing monitoring, tailored treatment plans, and extended care.
Machine learning (ML) has become a revolutionary resource in healthcare, providing innovative strategies to manage chronic illnesses better.
This article examines the impact of ML in addressing chronic illnesses on a worldwide scale.
Machine Learning In Healthcare
“In simple terms, ML means instructing computers to learn from data without direct programming.”
Machine learning, a branch of artificial intelligence (AI), utilises algorithms and statistical models that enable computers to carry out tasks without direct instructions.
In the context of healthcare, AI refers to the simulation of human intelligence processes by machines, especially computer systems.
On the other hand, machine learning is a subset of AI that focuses on developing computer programs that can access data and use it to learn for themselves.
In simple terms, ML means instructing computers to learn from data without direct programming.
Think of it as teaching a toddler to recognise a cat by showing it 10,000 pictures of cats.
“AI” and “machine learning” are often used interchangeably. In healthcare, machine learning algorithms can examine extensive datasets, recognise patterns, and generate predictions that aid in diagnosing, treating, and managing diseases—It’s just you having an exceptionally intelligent assistant who never steps away for a coffee break.
Applications Of Machine Learning In Managing Chronic Diseases
Early Detection And Diagnosis
A major contribution of ML in managing chronic diseases is its capacity to enable early diagnosis and detection.
Chronic illnesses typically progress slowly, and timely intervention can greatly enhance results.
ML algorithms can examine vast amounts of patient information, such as medical history, lab results, and images of medical scans like x-rays, mammographs, etc, to detect early indicators of chronic illnesses.
In advanced nations, electronic health records (EHRs) resemble healthcare journals, rich with intricate details of people’s health histories and health profiles.
ML models can forecast the onset of conditions such as diabetes with an accuracy of 85-90% by examining data including blood glucose levels, body mass index, and family history.
Organisations like IBM Watson Health and Google DeepMind are at the forefront of this innovative movement.
In many African countries, accessing healthcare is as difficult as finding Wi-Fi in the Sahara; it is almost unavailable and unreliable at best.
However, mHealth applications powered by ML are transforming the landscape.
For instance, CardioPad, a mobile ECG instrument created in Cameroon, employs ML to identify cardiovascular illnesses in remote regions.
In Senegal, AI4TB, a technology created by FIND, employs AI algorithms to identify TB in chest X-rays, facilitating early diagnosis in rural clinics lacking radiologists.
In India, where the doctor-patient ratio is significantly low, ML is used to predict the onset of diseases like diabetes and heart conditions, allowing for early intervention and better management.
mDiabetes uses SMS to gather patient information and utilises ML to detect diabetes early.
Who would have thought that your phone could act as a doctor?
“ML models can forecast the onset of conditions such as diabetes with an accuracy of 85-90% by examining data including blood glucose levels, body mass index, and family history.”
Personalised Treatment Plans
Chronic illnesses are like snowflakes; each one is uniquely different. Because of this, they frequently need customised treatment plans designed for the person’s specific traits, such as genetics, lifestyle, and existing health conditions.
ML plays a crucial role in customising treatments for each patient, instilling confidence in the populace.
Because when it comes to healthcare, one-size-fits-all might be great for stocks but not for treating chronic illnesses.
ML ensures that each patient receives a treatment plan tailored to their unique genetic makeup, lifestyle, and existing health conditions.
Personalised medicine is extremely popular in the West. Startups like Tempus use machine learning (ML) to examine genetic, molecular, and clinical information and recommend tailored cancer therapies.
At the same time, Insilico Medicine employs ML to enhance drug dosages, minimising side effects–It’s just like having a personal tailor for your medicine cabinet, designing medications specifically suited to you according to your genetic makeup and the molecular characteristics of the illness.
A key focus area for Tempus is genomic sequencing, which allows for the detection of genetic mutations or changes that could influence a patient’s disease progression or response to treatment.
Through its genomic testing services, Tempus provides important insights into the genetic factors that can affect cancer patient outcomes. By examining molecular and clinical information, Tempus aids in determining the best treatment choices for every patient.
This method corresponds with the principles of personalised medicine, in which therapies are customised to the specific genetic and molecular traits of a person’s condition.
Insilico employs deep learning methods to examine intricate biological systems and forecast the interactions of various molecules with particular targets in the body.
A key strategy of Insilico is to identify novel drug targets and validate them using AI. Insilico additionally focuses on slowing down ageing at a molecular scale.
Despite resource constraints, ML continues to have a significant impact in Africa. For instance, machine learning algorithms have been used to enhance antiretroviral therapy (ART) for HIV patients in South Africa.
ML can suggest the most efficient ART combinations by analysing factors such as medication access and patient compliance. It’s similar to having a personal pharmacist who additionally works as a data scientist on the side.
Syndicate Bio (Africa), Mediclinic Precise (South Africa), and Revna Biosciences (Ghana) are the leading startups in the race to advance precision medicine in Africa.
Mediclinic Precise provides DNA-driven diagnostic and clinical interpretation services, allowing healthcare professionals to tailor wellness, disease prevention, and treatment strategies based on personal genetic profiles.
Syndicate Bio runs cutting-edge genomics labs throughout Africa, offering services like sequencing, tumour profiling, liquid biopsy, and multi-omics data creation.
These services enhance personalised healthcare by allowing accurate diagnostics and promoting genomics-driven research and development.
Syndicate Bio enhances precision medicine initiatives, such as target identification and customised therapies, by integrating patient recruitment platforms and bioinformatics analysis.
Portable Devices Driven By AI And Machine Learning
Additionally, there is an increase in portable devices powered by AI-ML that assist in managing various chronic diseases, particularly those that are unpredictable and whose progression is difficult to measure, such as asthma and COPD.
Propeller Health is a platform that tracks medication use in inhalers through machine-learning algorithms.
It offers tailored asthma management advice derived from information gathered from the user’s inhaler.
Adherium (Hailie®) is another smart inhaler powered by AI that monitors usage and offers insights to enhance asthma management.
There are also AI-driven pulse oximeters; these gadgets measure oxygen saturation levels and employ AI to assess trends while providing tailored suggestions based on an individual’s respiratory health. This innovative approach is particularly helpful for handling ongoing respiratory conditions such as COPD or asthma.
Examples of such devices are the iHealth Pulse Oximeter, which not only tracks oxygen saturation but also delivers tailored insights on respiratory health patterns and notifies users when oxygen levels are critically low, and the Nonin Pulse Oximeter, which provides AI-enhanced analysis of oxygen levels and aids in managing respiratory issues by giving customised health insights.
Remote Monitoring And Telemedicine
Thanks to COVID-19, telemedicine transitioned from being a “nice-to-have” to “essential” quicker than you can say “quarantine”. Now, ML has further enhanced telemedicine.
In first-world countries, people often use wearable technologies such as ML-powered Apple and Fitbit watches to track vital signs and identify irregular heartbeats.
There are actually ML models that can predict a myocardial infarction up to an hour before it happens. It’s similar to possessing a crystal ball but with superior battery longevity.
Nowadays, there are even more sophisticated methods for remote monitoring.
Following COVID, there has been a heightened emphasis on the remote monitoring of vital signs, which is particularly crucial for patients with chronic illnesses, especially those not effectively managed by their doctors.
Numerous portable devices can monitor vital signs and various other physiological parameters. These are collectively referred to as “Medical Wearables” or simply wearables.
Some of the parameters that these devices can measure include:
Heart Rate (HR), Heart Rate Variability (HRV), Blood Pressure (BP), Electrocardiogram (ECG/EKG), Peripheral Oxygen Saturation (SpO2), Cardiac Output, Respiratory Rate (RR), Lung Capacity, Blood Oxygen Levels (SpO2), Sleep Apnea Detection, Stress Levels, Mental Fatigue, Brain Activity, Stress Levels, Mental Fatigue, Brain Activity, Mood Monitoring, Alcohol Intake, etc.
These parameters are measured and evaluated by machine learning models, which provide suggestions for managing chronic diseases and advise on individualised treatment and dosage plans for patients with chronic conditions.
Here are several AI-driven devices that facilitate remote observation of patients with chronic illnesses and improve telemedicine services:
Continuous Glucose Monitors (CGMs) Powered By ML
ML-powered Continuous Glucose Monitoring (CGM) uses sensors to monitor glucose levels continuously, day and night. AI algorithms examine trends and patterns within the data, notifying patients of possible problems before they escalate.
This gadget is especially beneficial for controlling chronic, uncontrolled diabetes. Devices like these include the Dexcom G6 and Freestyle Libre 2 (by Abbott). These CGM devices show an accuracy ranging from 90% to 98% in comparison to conventional lab-based blood glucose tests.
Portable Insulin Pumps Enhanced By AI
Portable insulin pumps are medical devices intended to supply insulin continuously or as needed for individuals with diabetes. They provide a substitute for multiple daily injections (MDI) and enhance insulin administration’s flexibility and accuracy.
There are various kinds of insulin pumps, each offering its distinct benefits. Traditional Insulin Pumps use tubing to link the insulin reservoir to the infusion set on the user’s body.
Examples consist of the Medtronic MiniMed 780G and Tandem t: slim X2. We also have Patch Pumps (Tubeless Pumps) that stick directly to the skin and are operated wirelessly through a handheld device or smartphone, such as Insulet Omnipod 5 and CeQur Simplicity.
Additionally, there are the Hybrid Closed-Loop Systems that work with continuous glucose monitors (CGMs) to automate insulin delivery (utilising machine learning algorithms) based on real-time blood glucose data, such as the Tandem t: slim X2 with Control-IQ and Medtronic MiniMed 780G.
A notable characteristic of portable insulin pumps is their ability to deliver basal (background) insulin continuously during the day, emulating the role of a healthy pancreas. They enable users to provide extra insulin for meals or to rectify elevated blood sugar levels.
Several modern pumps can link to CGMs for immediate glucose tracking and automatic insulin modifications, allowing users to establish customised basal rates, insulin-to-carb ratios, and correction factors.
How AI-ML Is Applied In Insulin Pumps
ML algorithms examine CGM data to predict blood glucose patterns and modify insulin administration instantly. These systems strive to maintain blood sugar levels within a set range by automatically adjusting insulin delivery by increasing, decreasing, or pausing it.
AI-ML can additionally forecast future high or low glucose levels and notify the user to take action (e.g. consume carbs or deliver a correction dose).
Machine learning algorithms adjust to the individual user’s specific insulin requirements as time progresses (Personalisation), enhancing insulin administration’s precision. AI can examine past glucose and insulin data to offer insights and suggestions for improved diabetes management.
Sleep And Brain Monitors Enhanced By ML (For Managing Chronic Diseases)
Rest is essential for controlling chronic illnesses like diabetes, heart disease, and hypertension. Sleep trackers powered by Machine learning observe sleep patterns, evaluate sleep quality, and provide insights to enhance overall health.
Examples of such gadgets include the Oura Ring and SleepScore Max.
Devices that monitor electroencephalograms (EEG) track the brain’s electrical activity.
AI-driven EEG devices combine EEG technology with artificial intelligence (AI) to enhance the assessment and understanding of brain function. These devices are valuable tools for diagnosing, monitoring, and treating various neurological and psychiatric disorders.
Examples consist of Emotiv Epoc X, Muse Headband, Ceribell EEG System, and NeuroNode Trilogy.
These devices employ AI algorithms to identify patterns in brain activity, offering immediate insights and customised feedback.
They are utilised for long-term brain ailments like epilepsy (identifying seizures), ADHD (observing attention span), mental health issues (monitoring stress, anxiety, and depression), sleep disorders (enhancing sleep quality), neurodegenerative illnesses (e.g., Alzheimer’s and Parkinson’s), stroke, traumatic brain injury (TBI), and ALS (facilitating communication for profoundly disabled individuals).
These devices are portable and wearable, providing convenience and accessibility for ongoing monitoring beyond clinical environments.
The combination of AI and EEG technology allows for the early identification of irregularities, customised treatment strategies, and enhanced patient results.
For example, gadgets such as DreamNet and Melon Headband emphasise sleep and cognitive function, while OpenBCI and Neurosity Notion 2 are employed in studies and the development of brain-computer interfaces.
Even with their benefits, difficulties like expenses, data confidentiality, and the requirement for precise AI models persist.
In general, EEG devices enhanced by AI are transforming neurology and mental health treatment by rendering brain monitoring more reachable, accurate, and practical.
Portable ECG Devices Enhanced By AI (for Personalised Heart Disease Management)
These mobile ECG (Electrocardiogram) devices utilise AI-ML algorithms to detect heart issues and offer tailored treatment suggestions based on a person’s specific heart rhythm patterns.
This is especially beneficial for treating arrhythmias, atrial fibrillation (AFib), and various heart conditions.
Some examples of these gadgets are KardiaMobile 6L (produced by AliveCor) and KardiaBand (designed for Apple Watch).
In Africa, where medical facilities are commonly sparse, mHealth applications powered by ML are revolutionary.
Babyl in Rwanda leverages machine learning to examine blood pressure information and offer remote consultations for patients with hypertension.
In the meantime, Kena Health in South Africa provides telemedicine services that utilise machine learning.
Predictive Analytics For Disease Advancement
Predictive analytics are just like a fortune teller for your health, as they forecast disease progression and spot patients at risk for complications, particularly with chronic illnesses.
ML significantly helps in predictive analytics for chronic disease advancement, allowing healthcare professionals to foresee disease progression, tailor treatment strategies, and enhance patient results through the examination of extensive datasets, such as electronic health records (EHRs), medical imaging, genomic information, data from wearable devices, and lifestyle elements.
ML algorithms have been employed to detect patterns and forecast the progression of chronic diseases like diabetes, cardiovascular disease, chronic kidney disease (CKD), Alzheimer’s, and COPD over time.
For instance, ML models can forecast the probability of a diabetic individual experiencing complications such as retinopathy or kidney failure or assess the risk of a heart attack in those with cardiovascular conditions.
These forecasts rely on elements like past health information, biomarkers, and behavioural patterns. These predictions have proven to be largely accurate, except in instances where early preventive pharmacotherapeutic measures and other actions have been implemented.
You could essentially claim that ML is the Nostradamus of the healthcare sector.
The application of ML in predictive analytics provides numerous advantages, such as early detection, tailored healthcare, and efficient resource management.
By recognising high-risk patients, healthcare professionals can introduce preventive strategies, customise treatments, and distribute resources more efficiently.
For example, ML algorithms can forecast the advancement of Alzheimer’s disease by examining cognitive test results and brain imaging information, enabling early treatment options.
Likewise, in chronic kidney disease, machine learning models can predict the deterioration of kidney function, assisting healthcare providers in determining the right time to start dialysis or alternative therapies.
For instance, in the U.S. and Europe, machine learning has been leveraged to forecast the advancement of conditions such as diabetes, heart disease, and Alzheimer’s by examining substantial datasets from electronic health records (EHRs), wearable technologies, and genomic information.
These models assist clinicians in recognising high-risk patients, tailoring treatment strategies, and enhancing resource distribution, ultimately leading to better patient outcomes and lowering healthcare expenses.
In Africa, using ML to predict chronic diseases encounters significant obstacles, such as inadequate healthcare infrastructure, disjointed data systems, and insufficient resources.
Despite these obstacles, there are encouraging efforts utilising ML to tackle chronic illnesses like HIV/AIDS, tuberculosis, and diabetes, which are widespread throughout the continent.
For example, machine learning models are utilised to forecast treatment results for HIV patients or to recognise individuals at risk of developing diabetes in settings with limited resources.
Mobile health (mHealth) technologies and wearable gadgets are increasingly popular, offering essential data for predictive analytics in areas with restricted access to conventional healthcare services.
While the West enjoys advanced technology and sophisticated data systems, many African countries prioritise tackling fundamental healthcare challenges and employing innovative, cost-effective approaches to enhance the management of chronic diseases. To bridge the gap between these regions, it’s essential to foster global collaboration, invest in health infrastructure, and create tailored machine-learning models that tackle the unique issues faced by African nations. By pursuing these initiatives, we can leverage the power of predictive analytics to fight chronic diseases on a global scale.
Obstacles And Constraints Of Machine Learning In Managing Chronic Diseases
Data Integrity And Accessibility
ML is as effective as the data used to train it. As the saying goes, garbage in, garbage out!
EHRs are commonly used in first-world countries, although data quality challenges such as missing or inaccurate information can still arise. Additionally, worries about data privacy may restrict data sharing.
The absence of standardised healthcare data systems in Africa poses a significant obstacle.
Numerous healthcare institutions continue to depend on paper records, complicating data collection and analysis. In Nigeria, for example, the adoption of EHR systems stood at between 18 and 23% as of March 2025. This remains a significant challenge that many African nations have yet to overcome.
The Future Of Machine Learning In Managing Chronic Diseases
Despite the obstacles, the outlook for ML in chronic illness management is more radiant than a supernova. Combining ML with other developing technologies, such as IoT, blockchain, and 5G, could transform the management of chronic diseases.
In Western countries, combining ML with IoT tools such as wearable sensors and smart home gadgets allows for ongoing tracking of patients with chronic illnesses. Additionally, blockchain technology can be utilised to protect and distribute healthcare information, while 5G networks facilitate real-time data assessment and remote consultations.
Merging ML with mobile technology in Africa can facilitate healthcare services to isolated and neglected regions. For instance, mHealth applications enhanced by machine learning can deliver telemedicine services, whereas blockchain technology can ensure the security of patient information.
Final Thoughts
Machine learning promises to revolutionise chronic disease management, providing innovative methods to identify, diagnose, and manage these illnesses.
From early identification and tailored treatment strategies to remote monitoring and predictive analytics, ML is already having a considerable effect on healthcare.
Nonetheless, issues concerning data quality, interoperability, ethical factors, and incorporation into clinical practice need to be tackled to fully harness the capabilities of ML.
Personally, I feel the organisations/startups that hold the key to the future of machine learning in healthcare in Africa are those that can develop innovative ways to acquire healthcare data legally and solve the problem of interoperability in healthcare data systems.
Community-driven data projects seem to be one of the ways this can be done.
