EARLY DETECTION OF CHRONIC CONDITIONS: THE ROLE OF AI, MACHINE LEARNING

LIFE SCIENCES

PURPOSE-DRIVEN ADAPTABLERESILIENT

Executive SummaryPeople of all ages with chronic diseases are at a higher risk of being impacted by COVID-19. According to a study conducted by the US Centers for Disease Control and Prevention (CDC), death rates are 12 times higher for coronavirus patients with chronic illnesses than for others who become infected. Another study reports that out of the total sample of COVID-19 patients hospitalized in the US, around 90 percent were chronic patients These statistics have put timely identification and proactive care for chronic patients at the top of the agenda.

Given most chronic diseases are asymptomatic, early diagnosis and detection can moderate the exposure of the pandemic and alleviate the burdens of healthcare providers and payers. In this paper, we suggest an innovative approach powered by the Business 4.0™ concept leveraging AI and machine learning, for the early detection of chronic diseases. The precise prediction of this model without physical clinical testing and the accuracy rate of up to 99 percent make it practical and unique.

PURPOSE-DRIVEN ADAPTABLERESILIENT

https://www.cdc.gov/mmwr/volumes/69/wr/mm6924e2.htm https://www.cdc.gov/mmwr/volumes/69/wr/pdfs/mm6915e3-H.pdf

Globally, chronic diseases including heart disease, diabetes, stroke, etc., are a leading cause of death. Although predicting chronic diseases in the initial stages has always been a priority for researchers, the current crisis has called for the urgent need to crack the code. Now more than ever, it has become imperative to identify patients at high risk by using data that can be collected remotely and easily while adhering to social distancing norms.

Chronic Diseases and Associated Burden

While managing chronic conditions post the diagnosis is not easy, this approach involving monitoring the potential patients on an ongoing basis and evaluating the ones at risk, presents a case for preventive care. In this method, benefits are achievable in the short term and it could also play a significant role in moderating the associated costs. Empowered by ML, this approach can help predict the risk condition with a much higher level of accuracy and precision, which is better than the current standard approach. Here, new risk prediction models are built by considering a wide range of demographic, biometric, clini-cal, and lifestyle factors for assessed individuals. This could prove to be instrumental in predicting related events such as hospitalizations and readmissions, which in the current scenario must be monitored and optimized.

Innovative Intelligence for Predicting Disease Patterns

Use Case – Predicting Kidney Chronic Disease with ML We conducted an ML project with real-world datasets to accurately predict kidney-related chronic disease, by taking 24 health-related attributes – including age, diastolic/systolic BP, RBC count, HB levels etc. – collected over a two-month period from 400 patients. Following is the step-by-step approach undertaken to develop a model that predicts the occurrence of chronic conditions related with kidney disease:

Fig. 1 – Correlation Matrix

Includes assessing attribute range, completeness of data, and identification of outliers. One of the most productive techniques that we followed is plotting a correlation matrix (figure 1) between various attributes and identifying the most impactful ones, which can play a vital role in predicting the risk condition. The higher the contrast of red color, the higher is the correlation.

Based on the matrix generated (above), it is evident that the correlation is significant with attributes like hemoglobin level, specific gravity, RBC count, hypertension status, and sodium level. Apart from this analysis, secondary literature also validates this, which makes our initial inferences even more dependable.

Manual data analysis

The next logical step would be to eliminate attributes that are not relevant for further analysis such as patient name, address etc. Identify similar outliers and handle missing data concerns. This would help avoid fallacious inferences and inaccurate modeling due to overfitting.

Data preprocessing

This is the hands-on part, where we worked on representing the trained models in the form of an equation, a function, or rule/decision charts.

Model training and testing

Model performance parameters like model accuracy and precision are evaluated and a confusion matrix was plotted, for each of the ML models applied.

Model performance assessment and

Next, we split our dataset into two subsets: training data and testing data. We have followed a random split technique with a ratio of 70:30.

Data split

Since labels are predefined in this scenario, we have leveraged classification models like decision tree, K-Nearest Neighbor (KNN), and Logistic Regression to train our dataset.

Model selection

Understanding the Results Based on the comparison of model accuracy, precision, and recall achieved from the different classifications applied, we plotted a bar chart (figure 2) to compare these performance parameters for various models.

It is important to note that we can afford false positives (Type 1) because this will only result in an additional diagnosis test, which is a one-time cost. However, we cannot afford “false negatives” (Type 2) as it will lead to sifting of patients with actual chronic conditions. This will result in huge treatment costs, years down the line, which is much higher than the diagnosis cost.

Considering all these factors, the Decision Tree is the best suited model. This model strikes a balance and scores good on all three parameters.

PERFORMANCE PARAMETER COMPARISION ACROSS MODELS

Accuracy

KNN

93.3

3

87.7

95.5

5

99.1

4

100

97.7

8

98.3

3

97.7

8

97.7

8

DECISION TREE LOGISTIC REGRESSION

Precision Recall

ConclusionWith chronic diseases incurring a huge burden not only in terms of causalities but also accounting for a big share of healthcare expenditure, this innovative prediction model if implemented can save huge costs. As per the Centers for Medicare and Medicaid Services (CMS), every year, around USD 1.65 trillion is spent on treating patients with one or more chronic diseases.

On a different note, a published study observed that during the SARS outbreak in 2003, chronic care hospitalizations initially dropped but skyrocketed later. Similar problems could come up in the case of the COVID-19 pandemic as well. Though predicting chronic diseases has been the goal of researchers for decades, the COVID-19 situation has called for a transformative approach. As much as the transformation comes by virtue of the input parameters used to make such predictions, it is the technology available today that has the potential to turn this around. There is need to shift the focus to first understanding the cause against studying the after-effects. This reimagined approach is also highly relevant in the current time with mounting evidence of vulnerability of the patients with chronic conditions in the face of the pandemic. With time, we can anticipate more advanced technologies that will help run such models using tremendous data sets and explore underlying patterns of disease impact. Simultaneously, this also opens new opportunities for medical devices and diagnostics companies, with AI application in this market expected to grow at a CAGR of over 43 percent and reach USD 27.6 billion by 2025. This, we believe, will lead to further accuracy enhancement in treatments, in turn adding opportunities for innovation.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7135451/ https://www.businesswire.com/news/home/20190304005403/en/Global-Artificial-Intelligence-Healthcare-Markets-2019-2025--

About the Author

Nitin Gupta

Nitin Gupta is an Assistant Consultant with Tata Consultancy Services and a part of the Life Sciences - CTO & Innovation group. In his current role, he facilitates innovation powered by emerging technologies within the unit. He has experience in areas such as Machine Learning, IoT, 3D Printing, and Social & Digital Marketing, with an in-depth understanding of evolving digital trends. He also looks into TCS Life Sciences Innovation Lab in Noida.

Nitin has helped conceptualize Innovation led frameworks and digital solutions across industries. He has published several thought leadership papers, blogs, and use cases in reputed national and international publications. He holds a bachelor's degree in mechanical engineering from Aligarh Muslim University and an MBA in marketing from Lal Bahadur Shastri Institute of Management, Delhi.

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