Authors :
Kshama S B; Ananya Dixit; Azra Rumana; Harshini K
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/5he8fkyw
Scribd :
https://tinyurl.com/2s3sujhb
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR1719
Abstract :
Electronic data has accumulated due to the
rising incidence of chronic illnesses, the complexity of the
relationships between various diseases, and also the
widespread use of computer-based technologies in sector
of health care. Doctors are encountering challenges in
accurately diagnosing illnesses and analysing symptoms
due to extensive volumes of data. In many of the reviews
of the present medical service frameworks, the focus was
on considering one disease at a time. The majority of
severe articles focus on a certain illness. These days, the
inability to identify the precise infection has led to an
increase in mortality. Indeed, a previously recovered
patient might experience reinfection with another illness.
Algorithms in machine learning (ML) have
demonstrated substantial capability in outperforming
traditional systems for diagnosing diseases, playing a
pivotal role in assisting medical professionals in the early
identification of elevated-risk diseases. In this literature,
the intention is to identify patterns across different types
of supervised and unsupervised ML models in disease
detection by assessing performance metrics.
Keywords :
Chronic Diseases, Disease Detection, Machine Learning, Performance Metrics.
References :
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- Sneha GramPurohit, Chetan Sagarnal “Disease Prediction using Machine Learning Algorithms” (IEEE) 2020
- Tapan Kumar, Pradyumn Sharma, Nupur Prakash “Comparison of Machine learning models for Parkinson’s Disease prediction” (IEEE) 2020
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Electronic data has accumulated due to the
rising incidence of chronic illnesses, the complexity of the
relationships between various diseases, and also the
widespread use of computer-based technologies in sector
of health care. Doctors are encountering challenges in
accurately diagnosing illnesses and analysing symptoms
due to extensive volumes of data. In many of the reviews
of the present medical service frameworks, the focus was
on considering one disease at a time. The majority of
severe articles focus on a certain illness. These days, the
inability to identify the precise infection has led to an
increase in mortality. Indeed, a previously recovered
patient might experience reinfection with another illness.
Algorithms in machine learning (ML) have
demonstrated substantial capability in outperforming
traditional systems for diagnosing diseases, playing a
pivotal role in assisting medical professionals in the early
identification of elevated-risk diseases. In this literature,
the intention is to identify patterns across different types
of supervised and unsupervised ML models in disease
detection by assessing performance metrics.
Keywords :
Chronic Diseases, Disease Detection, Machine Learning, Performance Metrics.
