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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Use of ML in HealthCare: Predictive Analytics and Diagnosis

Bhanu Venkata Krishna
Bhanu Venkata Krishna Last Updated : 14 Feb, 2023
7 min read

Introduction

Do you know that the majority of medical errors are caused by misdiagnosis? Because many diseases have similar symptoms, which is difficult for doctors to give accurate results. The main reasons for misdiagnosis are a lack of experienced doctors, lack of time with patients, lack of resources, etc. What if some technology can overcome the problems in healthcare? Sounds interesting, right? That’s where Machine Learning comes into the picture. This article will help you learn how machine learning is used in healthcare.
Learning Objectives:
  1. Overview of how machine learning helps the healthcare industry.
  2. Understanding the use of predictive analytics in the Healthcare industry.
  3. How machine learning helps in the diagnosis of various diseases using different techniques?
  4. Challanges in using ML in the healthcare sector and how to overcome those.
This article was published as a part of the Data Science Blogathon.

Table of Contents

  1. What is Machine Learning?
  2. How is Machine Learning Used in Healthcare?
  3. Machine Learning in Predictive Analysis
  4. Machine Learning in Diagnosis
  5. Challenges of using Machine Learning in Healthcare
  6. How to Overcome Challenges of Using Machine Learning in Healthcare?
  7. Conclusion

What is Machine Learning?

Machine learning (ML) is an artificial intelligence domain where we extract patterns from the data and make intelligent predictions on the new data according to the model our machine has learned. It is based on the idea that machines can learn from experience and improve their performance over time. There are three main types of machine learning
  • Supervised Learning: In Supervised learning, the model is trained with unlabeled data to predict outputs. It identifies patterns, anomalies, and relationships in data.
  • Unsupervised Learning: In Unsupervised learning, the model is trained with unlabeled data to predict outputs. It identifies patterns, anomalies, and relationships in data.
  • Reinforcement Learning: In Reinforcement learning, the model performs based on rewards received in the previous action.

How is Machine Learning Used in Healthcare?

Machine learning is used in health care in many ways. It can analyze large amounts of data and identify patterns that are not visible to humans. In healthcare, it is mainly used in predictive analytics and diagnosis to predict patient outcomes and improve the accuracy of diagnostic tools. It can help doctors to make better decisions, improve patient outcomes and reduce costs. It automates repetitive tasks, reduces errors, and provides more personalized care.
Healthcare

Source: wlpaperboat.com

Machine Learning in Predictive Analytics

Predictive analytics in healthcare predicts future patient outcomes and identifies what treatment to give patients based on their health condition. Standard techniques used in predictive analysis include machine learning, statistical modeling, and data mining. These techniques can be applied to data from various sources, including electronic health records and patient-generated data. A  model is trained on patient data, including demographics, medical history, and vital signs, to predict the likelihood of a patient being readmitted to the hospital within a specific period. Healthcare providers can use this information to address potential issues and proactively prevent readmissions. Predictive analysis can help doctors to make decisions about patient care and treatment. Some of the main applications of predictive analytics in healthcare are:
  • Disease Management: Predictive models are used to detect different types of diseases, such as heart disease, diabetes, and cancer, and take preventive measures early for patients who are at high risk. For example, Machine learning algorithms can analyze data from EHRs and other sources, such as medical history, lab results, and lifestyle information, to predict a patient’s risk of developing diabetes.
  • Readmission Prediction: Machine Learning models can analyze patient data and predicts who is at risk of being readmitted to the hospital after discharge. It helps healthcare providers to take preventive measures to reduce readmission rates.
  • Resource and Demand Forecasting: Predictive models can predict demand for resources such as beds, operating rooms, and staff. It can help healthcare providers to manage their staff and inventory levels more effectively.
Healthcare

Source: engineering.wustl.edu

These are typical applications, but predictive analytics can be applied in many other areas, depending on the healthcare organization’s specific needs and available data

Machine Learning in Diagnosis

Diagnosis is an essential step in the healthcare process as it helps to ensure that patients receive the appropriate treatment for their condition as soon as possible. Machine learning is used in diagnosis to analyze medical data and predict a patient’s health. It is impossible to analyze large amounts of data and predict diagnoses by doctors. Still, Machine learning can be used in diagnosis by analyzing patient data to make predictions about the presence of certain diseases. This includes analyzing medical images (such as X-rays, CT scans, and MRI) for signs of diseases or conditions and using data from electronic health records (such as demographics, lab results, and medical history) to predict the likelihood of a patient having a particular disease. The model is trained on a large dataset of labeled patient data and continuously improves its predictions as it receives more data. This can help healthcare providers make more accurate and efficient diagnoses. These are the applications of machine learning in diagnosis are: 
  • Image Analysis: Machine learning algorithms can analyze medical images, such as CT scans, MRIs, and X-rays, to detect signs of disease or injury. For example, a model trained on a dataset of lung CT scans can be used to identify lung cancer. We can also detect diabetic retinopathy, breast cancer, skin cancer, and heart disease diagnosis using machine learning image analysis.
  • Natural Language Processing (NLP): It extracts information from unstructured data such as medical reports, clinical notes, and electronic health records. For example, NLP techniques can extract information from electronic health records and create structured data sets, which can train predictive models to predict the likelihood of a patient developing a specific condition or disease.
  • Monitoring of Vital Signs: Machine learning algorithms can monitor vital signs such as heart rate and blood pressure to identify early warning signs of a health condition. For example, ML algorithms can monitor crucial signs by analyzing video. They can detect abnormal heart rates or breathing patterns by seeing the patient’s face.
Healthcare

   Source: assets.chaminade.edu

These are standard applications, but diagnosis is applied in many other areas based on healthcare organization needs and available data.

Challenges of Using Machine Learning in Healthcare

While there are many applications of machine learning in healthcare, there are also some challenges. They are:
  • Data Quality: High-quality data is essential for training and testing machine learning models. If the available data is of good quality, it can positively impact the performance of the models, and it will not predict accurate results.
  • Limited Data: The amount of data available for machine learning in healthcare is limited, particularly for rare diseases or conditions. With the limited data, we cannot train our models effectively.
  • Missing Data: Generally, healthcare organizations need more data. Training models or making accurate predictions can be complex when medical records have insufficient data.
  • Feature Engineering: Extracting relevant features from the data can be a time-consuming and challenging task. Identifying the most critical elements to include in a model requires domain expertise and an understanding the problem.
  • Privacy and Security: Healthcare data is highly sensitive and protected by strict regulations such as the Health Insurance Portability and Accountability Act (HIPAA). Ensuring the confidentiality and security of patient data is a significant challenge when working with machine learning in healthcare.
  • Complex and High-dimensional Data: Healthcare data, such as medical images and time-series data, can be complicated and high-dimensional. Designing and training models that can effectively handle such data is difficult.
  • Explain Ability and Interpretability: Many machine learning models, particularly deep learning models, are challenging to interpret and understand. This can be a problem in healthcare because these models’ decisions can have severe consequences for patients if they predict wrong results.
  • Model Deployment and Integration: Model deployment and integration of machine learning in healthcare can be a problem due to technical complexity, lack of standardization, limited resources, data privacy and security, limited adoption, and lack of trust.

How to Overcome Challenges of Using Machine Learning in Healthcare?

  • Improving Data Quality: Use data cleaning and preprocessing techniques to ensure the data used for machine learning is accurate, complete, and usable.

  • Ensuring Data Privacy and Security: Implement strict security measures, such as secure data storage, access control, and encryption, to protect patient data.

  • Interpretable Models: Use interpretable models and visualization tools to help healthcare providers understand the predictions being made by machine learning models.

  • Ethical Guidelines: Establish ethical guidelines for using machine learning in the healthcare and test models for discrimination and bias to avoid ethical issues.

  • Clinical Workflow Integration: Work closely with healthcare providers, machine learning experts, and IT professionals to develop systems that are usable, efficient, and effective.

  • Model Validation and Testing: Validate and test machine learning models to ensure they are accurate and reliable.

  • Data Collection: Focus on collecting large, high-quality datasets to improve the accuracy of machine learning models.

  • Model Explainability: Use explainable AI methods to improve the interpretability of machine learning models and increase trust among healthcare providers and patients.

  • Building a Multidisciplinary Team: Assemble a team with expertise in both the healthcare domain and machine learning to overcome the technical challenges of using machine learning in healthcare.

Conclusion

In this article, we have learned what machine learning is and the role of machine learning in healthcare, why it has been used in predictive analytics and diagnosis, its applications, and the challenges of using Machine Learning in Healthcare. Some of the Key takeaways from this article are:
  • ML models can analyze large amounts of data and identify patterns and predictions that would be difficult or impossible for human analysts to detect.
  • Machine learning has the potential to revolutionize the field of healthcare by enabling more accurate predictive analytics and diagnosis.
  • However, it is significant to note that the successful implementation of machine learning in healthcare requires strong collaboration between healthcare professionals and data scientists.
Thanks for reading this article! I hope you have gained some knowledge on how machine learning is used in healthcare. If you have any doubts, Please do Comment.
The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.
analytics in healthcare industryblogathondemand forecastingdiagnosisdisease managementhealthcaremachine learningpredictive analysis
Bhanu Venkata Krishna
Bhanu Venkata Krishna

I'm final year student of cse.As a final year student I'm dedicated to learning new technologies. I have strong foundation in programming, mathematics,and data analysis. Along with technical skills I have experience in working on projects and developing problem solving skills.

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