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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

Data Science In Healthcare

S
Sharvari Last Updated : 05 Apr, 2023
6 min read
Data Science In Healthcare 1
Photo by Online Marketing on Unsplash

A combination of science, technology, and medicine in the dynamic digital age has unveiled new data systems to improve statistics, improve healthcare and drug delivery, and improve health information reporting on clinical decisions.
Data science in health care has seen the latest and most rapid progress in 3 ways: 

  1. Using big data with a combination of large and complex data sets includes electronic medical records, social media, genomic information, and digital body data from wireless health devices.
  2. With new open-access efforts that seek to utilize the availability of clinical trials, research, and citizen science sources for data sharing.
  3. In the analysis techniques, especially of big data, including machine learning and artificial intelligence that can improve systematic and unstructured data analysis.

This article was published as a part of the Data Science Blogathon.

As new data sets are being developed, analyzed, and growing available, a number of key questions arise, including the following:

  • What is the quality of informal data processing? 
  • Is the use of unsaved methods in data processing with traditional software and hardware lead to data fragmentation and non-productive analysis? 
  • Will health care systems process and process large amounts of data, especially from new and community-based sources? 
  • Doctors too
    researchers are learning from new open-source and larger data statistics? 
  • And finally, how can they get the skills to building translation of the information in data science?

 

Disease Prevention and Predictive Medicine

Data Science In Healthcare _ prevention
Photo by Hush Naidoo on Unsplash

The best way to change health care is to identify risks and recommend prevention programs before health risks become a major problem. By wearing it with other tracking devices that pay attention to historical patterns and genetic information, you may be able to see the problem before it gets out of hand.

Data science analytical methods learn from historical data and make accurate predictions of results. They process patient data, make sense of clinical notes, find interactions, symptomatic associations, general adjectives, habits, diseases, and make predictions. The effects of certain biological factors such as genome structure or clinical variability are taken into account to predict the occurrence of specific diseases. Common causes include prediction of disease progression or prevention to reduce the risk and side effects. The main benefit is to improve the quality of life of patients and the quality of medical conditions.

Omada Health is a digital medical company that uses smart devices to create customized behavioral plans and online training to help prevent chronic health conditions, such as diabetes, high blood pressure, and high cholesterol. 

On the mental health side, Canada’s new start-up, Awake Labs, is tracking data on children with autism in dress, informing parents before the meltdown.

Diagnosis
The National Academies of Sciences, Engineering, and Medicine estimates that some 12 million Americans receive incorrect diagnoses, sometimes with life-threatening consequences.

Medical Thinking And Medical Imaging

Data Science In Healthcare - medical thinking and imaging
Photo by National Cancer Institute on Unsplash

The healthcare sector is reaping huge benefits from the application of data science applied to medical thinking. There are many things to research in this area, and one of the best studies is Big Data Analytics, published in BioMed Research International. According to this study, popular methods of thinking include magnetic resonance imaging (MRI), X-ray, computed tomography, mammography, and so on. Many methods are used to deal with the variation, adjustment, and magnitude of these images.

A lot more is improved to improve image quality, extract data from photos efficiently, and provide a more accurate translation. In-depth learning algorithms increase diagnostic accuracy by learning from previous examples and suggesting better treatment solutions.

IBM estimates that medical images contain about 90% of the total medical data. Doctors use imaging therapy to get a clearer understanding of body parts.
Also, evaluate the function of other organs to diagnose and treat any disorder or disorder. The insights gained from these images can make a difference in a patient’s treatment.

The most popular imaging techniques focus on developing, dissecting, and eliminating denoising that allows for in-depth analysis of the anatomy, as well as the diagnosis of various diseases.
The most promising applications are for tumors, artery stenosis, delineate, etc. Different methods and frameworks contribute to medical thinking in a variety of areas. Hadoop, a popular analytical framework, uses MapReduce to obtain appropriate parameters for tasks such as lung tissue planning. It works with machine learning methods, vector support equipment (SVM), content-based image guidance, and wavelet analysis with strong texture separation.

 

Making Medicines

Data Science In Healthcare making medicines
Photo by Olga Kononenko on Unsplash

The process of drug discovery is very complex and involves many areas. Big ideas are often tied to billions of tests, big money, and time. On average, it takes 12 years to get a prescription. Scientific algorithms and machine learning data simplify and streamline this process, adding perspective to each step from the initial testing of drug chemistry to predicting success rate depending on biological factors. Such algorithms can predict how the compound will work in the body using advanced mathematical modeling and simulation instead of a “lab test”.

The idea behind the discovery of computer drugs is to create a simulation of computer models as a viable network for life making it easier to predict future results with high accuracy. Allows selection, which test should be performed and incorporates all new information into the ongoing learning loop. Analogous techniques are used to predict the adverse effects of certain chemical compounds.

Computerized drug discovery also improves the collection and use of a wide variety of historical information during the drug manufacturing process. Combining genetic research with protein-binding data can produce amazing results. In addition, it allows chemical experiments against all possible combinations of different cell types, genetic mutations, and other conditions. Using this data, unsupervised learning, and technology as the next generation sequence, enables scientists to create models that predict the outcome from independent variations.

 

Virtual Assistant

Virtual Assistant
Photo by BENCE BOROS on Unsplash

The effectiveness of the treatment approach builds on the idea that in many cases patients do not need to visit doctors in person. Using a mobile app can provide a more effective solution by “bringing a doctor to the patient” instead. AI-enabled mobile applications can provide basic health care support, usually like chatbots.

Simply describe your symptoms, or ask questions, and get important details about your medical condition based on a broad network of symptoms and causes. Apps may remind you to take your medication on time, and if necessary, give an appointment with your doctor. This approach promotes a healthy lifestyle by encouraging patients to make healthier decisions, saves their time waiting in line for appointments, and allows physicians to focus on more serious situations.

Machine learning algorithms use processing and natural language to provide accurate information, create a complex map of the user’s status, and provide a personalized experience. The most popular applications these days are Yours.MD, Babylon Health, Ada, and so on.

In this way, the most appropriate customer support is built on the obvious reliance that does not rely entirely on machines in health care. Therefore, the great task of machine learning is to find the perfect balance between physicians and computers. The key to automation is simple methods, as we have just explained, and give professionals the ability to focus on more complex problems.

 

About Me:

Technical Writer 👩‍💻 | AI Developer😎| | Avid Reader 📖 | Data Science ❤️ | Open Source Contributor 🌍

Hey, I am Sharvari Raut. I love to write!

Connect with me on:

Twitter: https://twitter.com/aree_yarr_sharu

LinkedIn: https://t.co/g0A8rcvcYo?amp=1

The media shown in this article on Data Science in Healthcare are not owned by Analytics Vidhya and is used at the Author’s discretion. 

S
Sharvari

I am Sharvari Raut. I love to write. I am a final year student in Computer Science and Engineering from NCER Pune. I have worked as a freelance technical writer for few startups and companies. Having 2 yrs of experience in Technical Writing I have written over 100+ technical articles which are published till now. Writing for Analytics Vidhya is one of my favourite things to do.

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Responses From Readers

Clear

Harish Nagpal
Harish Nagpal

Nicely written

123

Priya
Priya

Very good quality content. Writing to get more updates from you.

123

SarthakRoy
SarthakRoy

Health care industry are used data science for the developing or maintaining the data of the drug related or patient related data operation .making medicine formula or other information are manipulating better way or fetching best way. Thanks for providing this valuable topic Keep sharing

123

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