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Unleashing The Most Demanding Data Science Projects In Healthcare

By Vineeth Kumar Category Data Science Reading time 11 mins Published on Apr 17, 2023

What Are Some Good Data Science Projects in Healthcare to Target in 2023?

A collection of healthcare data science projects in the portfolio is vital if you want a lucrative career transition within the healthcare industry. The opportunities for a data scientist in healthcare are endless, but the job market competition is harder. If you are in the healthcare sector and want to stay in a win-win position, you need the fusion of two things in your portfolio.

'Strong data science concept + real-world and unique data science projects in healthcare.'

The healthcare industry is the biggest resource of unstructured data. But, just like other industries, the role of data science in healthcare is fastly earning its significance. In fact, without proper data analysis, healthcare industry operations are almost impossible. And without structuring the data, precise analysis is not at all possible. Consequently, the healthcare sectors of India now need plenty of healthcare data scientists for disease prediction, medical imaging, and analysis of scanned images and data-driven expertise.

Read the related blog: The 7 Best Data Science Project Ideas to Get Hired by Top MNCs

The Curriculum vitae (CV) for a position as a data scientist in the healthcare sector could be improved by data science project topics and ideas for the prediction of patient health, treatment & medical image analysis, and so on.

What are some advanced data science projects in healthcare?

In the healthcare industry, plenty of data scientist jobs are available. From the utilization of digital scanning, and advanced analytics to electronic health records, data science has proved its importance everywhere. It's a perfect moment for an emerging data scientist to start looking into them.

Now let's dive into the seven best-advanced data science-related projects in healthcare in 2023.

1. Prediction on readmissions

2. Pneumonia Detection using CNN (92.6% Accuracy)

3. Diabetes Prediction - Voting classifiers

4. Heart Attack Prediction

5. Visceral Adipose on Pregnancy

6. Body Fat Prediction (99.5%)

7. Breast cancer CNN Densenet

1. Prediction on readmissions

Tools Utilized: Python, sklearn, Binary classification

Dataset: Kaggle

The dataset includes clinical services offered for over ten years (1999–2008) at 130 American hospitals and integrated healthcare networks. There are more than 50 characteristics that indicate patient as well as hospital results. The database was checked for interactions that complied with the following requirements, and information was then extracted from it.

(1) This is a routine hospital visit (consultation).

(2) This is a diabetic incident, meaning diabetes was recorded as a diagnosis during the interaction.

(3) The visit lasted not more than 14 days and no less than one day.

(4) Laboratory tests were conducted during the visit.

(5) Prescription of drugs that were provided during the interaction.

The data includes features like

  • The patient number

  • Gender, race, and age

  • Admission Type

  • Duration of hospital stay

  • Admitting physician's medical specialty

  • The number of lab tests conducted

  • The results of the HbA1c test

  • The diagnosis; the total number of medications he number of diabetic medications

  • The number of inpatients, outpatient, and medical emergency visits during the year before hospitalization.

There are two key targets:

  • To develop a trustworthy and effective machine learning algorithm that could be utilized to forecast whether diabetic patients would require readmission within a month.

  • To identify which factors are most critical in the readmission of diabetes patients.

By considering demographic data, changes in medical specialties, and other factors, this study hopes to advise physicians. The advice is regarding the probability of early readmission for diabetic patients.

Hospitals and the healthcare industry may recognize areas for development. They can also set indicators to prevent readmissions and enhance patient outcomes by forecasting readmission rates.

Moreover, forecasting readmission rates might assist in locating high-risk individuals who can profit from extra assistance and interventions.

This can be one of the impressive healthcare data science projects that can improve elderly and chronic disease care.

Tips to be on the top preference of the interview panel: The best will be if you collect your local hospitals' or regional hospitals' data and do a completely fresh project on the same.


An image shows the code to create a list of top most features based on importance.

A Stacked bar chart with a horizontal axis ranging from 0.00 to 0.10 and a vertical axis with training data shows the training score with high accuracy.

A snippet shows the codes for the predicting test accuracy model on the training data.

A snippet shows the codes for the predicting test accuracy model on the training data.

Output conclusion

Three models are implemented throughout the modeling phase:

  1. Logistic Regression

  2. Random Forest, and

  3. XGBoost.

Assuming that the predictive accuracy of these models is comparable. Also, we can consider that the logistic regression method is easier to interpret than tree-based models.

Then, it could potentially provide clinicians with additional information about whether a patient will be readmitted before 30 days.

Particularly, elderly individuals (70+) tend to weigh more, which makes sense. A1Cresult and medical specialties like repaglinide appear to be significant factors in readmission, in addition to the number of inpatient and discharge types.

2. Pneumonia Detection using CNN (92.6% Accuracy)

Tools Utilized: Python sklearn, CNN

Dataset: Kaggle

The dataset is divided into the following three folders: train, test, and val.

Each category (Pneumonia/Normal) has a separate subfolder inside the dataset.

There are two categories (Pneumonia/Normal) and 5,863 X-Ray images in JPG format.

Chest X-ray medical imaging (anterior-posterior) was chosen from retrospective cohorts of child patients. All of them had an age range of one to five. The location chosen organization was the Guangzhou Women and Children's Medical Hospital in Guangzhou.

All chest X-ray screenings were done as part of the regular clinical treatment provided to patients.

All chest radiographs are originally inspected for quality checks before being eliminated from the chest X-ray (medical) image analysis.

Before the evaluations of the pictures could be utilized to train the AI system, they were evaluated by two qualified physicians.

A code for countplot evaluation for pneumonia.

A bar graph with horizontal axis labelled as Pneumonia and Normal and vertical axis ranging from 0 to 4000, labelled as count, shows the count plot output.

A screenshot shows the code for developing an X-ray graph.

An grided X-ray image of Pneumonia shows two axis.  The horizontal axis ranges from 0 to 140, and the vertical axis ranges from 140 to 0.

A grided X-ray image of a normal human chest shows two axis. The horizontal axis ranges from 0 to 140, and the vertical axis ranges from 140 to 0.

The dataset has to be artificially enlarged to prevent the overfitting issue. The objective is to replicate the variations by making minor adjustments to the training data. Here you need to apply data augmentation.

Data augmentation strategies are methods that modify the training data in a style that modifies the array format while preserving the label.

Grayscales, vertical flips, horizontal flips, random cropping, translations, color jitters, rotations, and other common augmentations are used frequently.

Users might quickly double or triple the range of training instances and build a strong model by applying these modifications to the training data.

To execute data augmentation the following steps should be followed:

  • Randomly rotating a few training pictures by 30 degrees.

  • Randomly Zooming by 20% a few training pictures.

  • Randomly shifting images by 10% of the width horizontally.

  • Randomly shifting images by 10% of the height vertically.

  • Randomly flipping the pictures horizontally. Once the model is ready, the training dataset will be fitted.

Code :

An snippet  shows the accuracy code model to detect pneumonia.

A table shows the output for accuracy detection model.

A snippet shows  the print command for classification report for pneumonia and a normal person.

A snippet shows the output for pneumonia class and a normal class using the classification model.

Pro Tips: To make such a project more relevant to the contemporary scenario, you can collect data that are relevant to any trending descents like Covid-19.

3. Diabetes Prediction Using Voting Classifiers

Tools Utilized: Python, KNN, Random Forest, Logistic Regression, sklearn

Dataset: Kaggle

According to epidemiological data, there seem to be currently more than 463 million diabetics patients globally, and in 2019 poll forecasts that number will increase to 700 million by 2045. Almost 720,000 people have been diagnosed with diabetes in Greece; the majority of them have type II diabetes, according to estimates.

According to the information above, as the world's population and average life expectancy rise, diabetes statistics will continue to rise significantly.

New machine learning and artificial intelligence calculation procedure have also emerged in addition to offering unique ways of illness detection. Such ways include postprandial glucose or hematological analysis of glycated hemoglobin or next-generation alleles at the genomic level.

By considering the patient's medical history, these cutting-edge techniques could be utilized to determine a predictive value for the probability of illness initiation.

In this analysis, machine learning techniques, including Logistic Regression, Random Forest, and the K-Nearest Neighbor (KNN) classifier, are utilized. Such models help to determine if an individual has a higher probability of obtaining diabetes mellitus. Everything is done based on a variety of measures such as body mass index, blood insulin levels, blood pressure, etc.

The codes show Logistic regression, Random forest classifier, and KNN classifier.

When the fundamental machine learning models have been trained, an estimator called a voting classifier combines the results of all the models to produce a prediction.

A final and more accurate conclusion could be produced using cumulative prediction criteria and a voting choice for each estimator result, improving the prediction's accuracy.

A code shows a voting classifier model for diabetes prediction.

Output conclusion

Finally, users observe that the model's accuracy marginally rises with the combination prediction of the voting classifier compared to the methods separately. The F1 score, therefore, equals roughly 86.4%, and the ultimate accuracy is about 85.3%.

This is one of the most trending data science project topics.

4. Heart attack prediction

Tools Utilized: Python, sklearn, Logistic regression

Dataset: Kaggle

Identify and investigate the factors that significantly affect the frequency of heart attacks. Build a model and make predictions using the information as well.

A code shows the groupin, cutting segment, and sortin data into a bin.

Executing EDA and modeling

  • Explain how cholesterol levels and the target variable are related.

  • What may be inferred regarding the association between peak exercise and the risk of a heart attack?

  • Is thalassemia a significant factor in CVD? How do the other variables impact the probability of CVD?

  • To interpret the connection between all the supplied variables, use a pair plot.

  • Use the confusion matrix to validate the findings after doing logistic regression and predicting the result for test data.

Code :

A code shows a logistic regression model for diabetic patients to predict accuracy.

A code shows logistic testing accuracy of 90.0% using the SKlearn library.

A table shows a classification report for the accuracy avg, macro avg, and weighted avg.

Output Conclusion

The model is excellent at predicting the data from the classification report and the confusion matrix. According to industry standards, sensitivity and specificity are particularly great.

If you are learning data science and have industry experience in the healthcare sector, then this will be great to draw the interviewer's attention.

5. Visceral Adipose on Pregnancy

Tools Utilized: Python, pywaffle - quiet, sklearn

Dataset: Kaggle

When the body is not able to generate enough insulin while a person is pregnant, then it leads to gestational diabetes. The hormone insulin, which is produced by the pancreas, operates as a key to allow blood sugar to enter the body's cells to serve as energy.

The codes shows pass axis to make waffle chart.

The codes show the pass axis to make a waffle chart for plotting the graph.

An snippet shows two different pie-charts and waffle charts for ethnicity distribution and diabetes mellitus distribution, represented in blue and orange colors.

An snippet shows two different pie charts and waffle charts for the type of delivery distribution and gestational dm distribution, represented in blue and orange colors.

A code shows the number of outliers detected using the Sklearn library.

The sample below was intended to demonstrate outliers, but because just a single outlier was detected, it won't be displayed in the charts below.

A code shows the plotting of the training column graph using KDEs.

A normal distribution graph with horizontal axis ranges 0 to 50, labelled as age, and vertical axis ranging from 0.00 to 0.06, labelled as density, shows the KDE plot.

A normal distribution graph with horizontal-axis ranges 0 to 180, labelled as Mean systolic BP and vertical-axis ranging from 0.000 to 0.0, labelled as density, shows the Mean systolic BP KDEs.

A normal distribution graph with horizontal-axis ranges 250 to 425 labelled as gestational age birth and vertical-axis ranging from 0.000 to 0.025 labelled as density shows the gestational age birth KDEs.

Output conclusion

Interventions during and right after pregnancy provide significant chances to enhance the lives of pregnant women and their children today. It also lowers the prevalence of diabetes in the coming generations.

It is possible to avoid Type 2 diabetes in two generations by screening for it and managing it properly during pregnancy. One of the factors contributing to GDM's low priority in the public health care system is a lack of knowledge in society.

6. Body Fat Prediction (99.5%)

Tools Utilized: Python, sklearn, Linear Regression, Random Forest

Dataset: Kaggle

To demonstrate multiple regression algorithms, this data set may be utilized. It is beneficial to have simple methods for estimating body fat when measuring body fat accurately is not challenging or costly.

The quantity of mass per unit of volume of a material is referred to as its density. It is a tangible quality that may be used to distinguish and describe different materials.

A screenshot show the code for plotting the density graph.

A density graph with a horizontal-axis ranging form 0.98 to 1.12 and a vertical-axis ranging form 0.0 to 17.5 shows a density curve.

Before preprocessing procedure

A graph with a horizontal axis of 0 and a vertical-axis ranging from 150 to 350 shows a Box plot with outliers before preprocessing of data.

A graph with a horizontal-axis of 0 and a vertical-axis ranging from 120 to 240 shows a Box plot with zero outliers after preprocessing of data.

After the preprocessing process,

A code for weight detection to remove the outliers after preprocessing.

A codes shows the box plot for weight after preprocessing.

A graph with a horizontal-axis of 0 and a vertical-axis ranging from 120 to 240 shows a Box plot with zero outliers after preprocessing of data.

We can observe that the data contain an outlier that might cause the model to diverge. One option to address this is to substitute this number with the dataset's mean. Let's do so before we view the boxplot.

A code shows output for body fat prediction.

A code for model prediction for r2_score.

Output Conclusion

  • Datasets from the provided link must first be imported.

  • The basic EDA process to be executed.

  • Detecting the outliers as shown above (before and after preprocessing).

  • Finally, predicting the output.

7. Breast cancer CNN Densenet

Tools Utilized: Python, sklearn, tensorflow, keras

Dataset: Kaggle

At the University School for Advanced Studies IUSS Pavia, this academic competition targets to support students as they go through the Machine Learning in Healthcare program.

Developing a model that can automatically differentiate cancer from benign tumors in breast ultrasound (US) examinations is the goal of the project.

A code for Malignant and benign to predict breast cancer CNN densenet.

A snippet shows two categories of medical images, benign and malignant. In benign, the horizontal axis ranging 0 to 600, and the vertical axis ranges from 500 to 0. Similarly, in malignant, the horizontal axis ranges from 0 to 400, and the vertical axis ranges from 400 to 0.

The Code shows the model summary using the Keras sequential.

A snippet shows the output for the model sequential based on total params, trainable params and Non-trainable params.

A code shows a model prediction for probability factor depending on probability.min and probability.max.

A chart shows the output for model prediction probability.

Output Conclusion

Implementing several risk variables in breast cancer prediction modeling may assist in the early identification of the illness.

This also helps in the development of essential treatment protocols. Disease management is successful with the collection, storage, and administration of various data as well as intelligent systems depending on several aspects for predicting breast cancer.


So, these are a few data science projects in healthcare. Understanding these projects with in-depth knowledge can help boost your overall data science learning process.

In a nutshell, we can say that the best decision in the current situation will be to pursue a profession in the field of artificial intelligence or machine learning but without changing your expertise domain. Especially if you are in the healthcare sector. An ample scope lies to build a rewarding career as a data scientist in this sector.

The smartest approach can be a primitive understanding of statistics and coding to pursue a career in Data Science and AI which will help you to gain systematic Career growth.