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Machine Learning in Healthcare Analytics: A State-of-the-Art Review

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Abstract

The use of machine learning (ML) models have become a crucial factor in the growing field of healthcare, ushering in a new era of medical research and diagnosis. This study rigorously reviews research publications published in reputable journals during the last five years. The pace and dynamic nature of machine learning in the healthcare domains demonstrated by the arduous criteria, which are used to sort through these articles. Disease-centric analysis uncovered a wide range of deep learning and machine learning models which are designed to address particular medical problems. Convolutional neural networks (CNNs), one of the most complex deep learning architectures, coexist with more conventional statistical models like logistic regression and support vector machines. CNNs are particularly prominent when it comes to disorders that need picture processing, which highlights the significant influence of deep learning in deciphering complex medical patterns. The popularity of ensemble methods, such as Random Forest, Gradient Boosting, and AdaBoost, indicates that their ability to combine predictive capability and strengthen model resilience is well acknowledged. Hybrid techniques, which integrate the advantages of many models, provide novel approaches to tackle distinct healthcare problems. This research also sheds light on a nuanced approach for model selection, wherein deep learning models performs well with huge datasets and image analysis, while statistical and ensemble models provides better results with numerical and categorical data. The adaptability needed in healthcare analytics is shown by hybrid models, which frequently combine standard models for classification with deep learning for feature extraction. The present review can endow problems related to ML in healthcare domain, possible solutions, potential directions and some knowledge to the researchers working in this field.

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Abbreviations

AB:

AdaBoost

ABC:

Artificial bee colony

AC:

Agglomerative clustering

BIRCH:

Balanced iterative reducing and clustering using hierarchies

BN:

Bayes net

BP-NN:

Back propagation neural network

CFS:

Correlation-based feature subset

DBSCAN:

Density-based clustering

Deep AE:

Deep autoencoders

DL:

Deep learning

EM:

Expectation maximization

FCM:

Fuzzy C-means clustering

FPSOCNN:

Fuzzy particle swarm optimization convolution neural network

GB:

GradientBoost

GBDT:

Gradient boosted decision trees

GMM:

Gaussian mixture model

HC:

Hierarchical clustering

IPCT:

Improved profuse clustering technique

KM:

K-means

LDA:

Linear discriminant analysis

LM:

Linear method

ML:

Machine learning

mRmR:

Maximum relevance minimum redundancy

NB:

Naive bayes

PART:

Projective adaptive resonance theory

PNN:

Probabilistic neural network

PSO:

Particle swarm optimization

QDA:

Quadratic discriminant analysis

RBE:

Restricted Boltzmann machine

RFNN:

Structured receptive field neural network

SC:

Spectral clustering

SCC:

Simultaneous clustering and classification

SDL:

Synergic deep learning

SGB:

Stochastic gradient boosting

SMP:

Sum of the maximal probabilities

VC:

Voting classifier

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  1. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India

    Surajit Das & Biswajit Sahoo

  2. Department of Computer Science & Engineering, Silicon Institute of Technology, Bhubaneswar, India

    Samaleswari P. Nayak

  3. Department of Computer Science and Engineering, School of Technology, GITAM Deemed to be University, Hyderabad, India

    Sarat Chandra Nayak

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Das, S., Nayak, S.P., Sahoo, B. et al. Machine Learning in Healthcare Analytics: A State-of-the-Art Review. Arch Computat Methods Eng (2024). https://doi.org/10.1007/s11831-024-10098-3

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