# Efficient Analysis of COVID-19 Clinical Data using Machine Learning Models

arXiv: https://arxiv.org/abs/2110.09606 · HTML: https://mlsanity.com/paper/2110.09606/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 58 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Builds on (most cited first, 15 of 15)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Spike2Vec: An Efficient and Scalable Embedding Approach for COVID-19 Spike Sequences](https://mlsanity.com/paper/2109.05019/) (2021)
- [A k-mer Based Approach for SARS-CoV-2 Variant Identification](https://mlsanity.com/paper/2108.03465/) (2021)
- [Predicting Attributes of Nodes Using Network Structure](https://mlsanity.com/paper/1912.12264/) (2019)
- [Effective and scalable clustering of SARS-CoV-2 sequences](https://mlsanity.com/paper/2108.08143/) (2021)
- [Effect of Analysis Window and Feature Selection on Classification of Hand Movements Using EMG Signal](https://mlsanity.com/paper/2002.00461/) (2020)
- [Short-Term Load Forecasting Using AMI Data](https://mlsanity.com/paper/1912.12479/) (2019)
- [Combinatorial Trace Method for Network Immunization](https://mlsanity.com/paper/1912.12070/) (2019)
- [A Multi-cascaded Model with Data Augmentation for Enhanced Paraphrase Detection in Short Texts](https://mlsanity.com/paper/1912.12068/) (2019)
- [Language Independent Sentiment Analysis](https://mlsanity.com/paper/1912.11973/) (2019)
- [A Multi-cascaded Deep Model for Bilingual SMS Classification](https://mlsanity.com/paper/1911.13066/) (2019)
- [Cache Replacement Algorithm](https://mlsanity.com/paper/2107.14646/) (2021)
- [TeliNet: Classifying CT scan images for COVID-19 diagnosis](https://mlsanity.com/paper/2107.04930/) (2021)
- [Locally Weighted Mean Phase Angle (LWMPA) Based Tone Mapping Quality Index (TMQI-3)](https://mlsanity.com/paper/2109.08774/) (2021)

## Built on by (most cited first, 6 of 6)

- [A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?](https://mlsanity.com/paper/2304.04780/) (2023)
- [PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences](https://mlsanity.com/paper/2201.02273/) (2022)
- [Efficient Approximate Kernel Based Spike Sequence Classification](https://mlsanity.com/paper/2209.04952/) (2022)
- [Reads2Vec: Efficient Embedding of Raw High-Throughput Sequencing Reads Data](https://mlsanity.com/paper/2211.08267/) (2022)
- [Evaluating COVID-19 Sequence Data Using Nearest-Neighbors Based Network Model](https://mlsanity.com/paper/2211.10546/) (2022)
- [BioSequence2Vec: Efficient Embedding Generation For Biological Sequences](https://mlsanity.com/paper/2304.00291/) (2023)

## Abstract (arXiv)

Because of the rapid spread of COVID-19 to almost every part of the globe, huge volumes of data and case studies have been made available, providing researchers with a unique opportunity to find trends and make discoveries like never before, by leveraging such big data. This data is of many different varieties, and can be of different levels of veracity e.g., precise, imprecise, uncertain, and missing, making it challenging to extract important information from such data. Yet, efficient analyses of this continuously growing and evolving COVID-19 data is crucial to inform -- often in real-time -- the relevant measures needed for controlling, mitigating, and ultimately avoiding viral spread. Applying machine learning based algorithms to this big data is a natural approach to take to this aim, since they can quickly scale to such data, and extract the relevant information in the presence of variety and different levels of veracity. This is important for COVID-19, and for potential future pandemics in general. In this paper, we design a straightforward encoding of clinical data (on categorical attributes) into a fixed-length feature vector representation, and then propose a model that first performs efficient feature selection from such representation. We apply this approach on two clinical datasets of the COVID-19 patients and then apply different machine learning algorithms downstream for classification purposes. We show that with the efficient feature selection algorithm, we can achieve a prediction accuracy of more than 90% in most cases. We also computed the importance of different attributes in the dataset using information gain. This can help the policy makers to focus on only certain attributes for the purposes of studying this disease rather than focusing on multiple random factors that may not be very informative to patient outcomes.