Authors :
Indrajeet Acharya; Harish Yadav; Abhibhav Jadhav; Nithish Kumar Naicker; Sabanaz S. Peerzade
Volume/Issue :
Volume 8 - 2023, Issue 5 - May
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/44t2u8es
DOI :
https://doi.org/10.5281/zenodo.8351030
Abstract :
The healthcare industry is increasingly
concerned about medical errors, which are the leading
cause of death worldwide and also compromise patient
safety. This medical error is even more serious in
developing countries where healthcare is not supported
by technology.Therefore, this study aims to assess physicians’
perceptions towards electronic prescription
implementation.Accurate and on-time analysis of any health-related
problem is important for the prevention and treatment of
theillness. The traditional way of diagnosis may not be
sufficientin the case of a serious ailment. Developing a
medical diagnosis system based on machine learning
(ML) algorithms for prediction of any disease can help in
a more accurate diagnosis than the conventional method.
We have designed a disease prediction system using
multiple ML algorithms. The dataset used had more than
41 diseases for processing. Based on the symptoms, age,
and gender of an individual, the diagnosissystem givesthe
output as the disease that the individual might be
suffering from. The Naïve Bayes algorithm gave the best
results as compared to the other algorithms. The accuracy
of the Naïve Bayes algorithm for the prediction was 99%.
Our diagnosis model can act as a doctor for the early
diagnosis of a disease to ensure the treatment can take
place on time and lives can be saved.
Keywords :
Healthcare, Prescription System, Treatment, Diagnosis, Machine Learning, Symptoms, Naive Bayes.
The healthcare industry is increasingly
concerned about medical errors, which are the leading
cause of death worldwide and also compromise patient
safety. This medical error is even more serious in
developing countries where healthcare is not supported
by technology.Therefore, this study aims to assess physicians’
perceptions towards electronic prescription
implementation.Accurate and on-time analysis of any health-related
problem is important for the prevention and treatment of
theillness. The traditional way of diagnosis may not be
sufficientin the case of a serious ailment. Developing a
medical diagnosis system based on machine learning
(ML) algorithms for prediction of any disease can help in
a more accurate diagnosis than the conventional method.
We have designed a disease prediction system using
multiple ML algorithms. The dataset used had more than
41 diseases for processing. Based on the symptoms, age,
and gender of an individual, the diagnosissystem givesthe
output as the disease that the individual might be
suffering from. The Naïve Bayes algorithm gave the best
results as compared to the other algorithms. The accuracy
of the Naïve Bayes algorithm for the prediction was 99%.
Our diagnosis model can act as a doctor for the early
diagnosis of a disease to ensure the treatment can take
place on time and lives can be saved.
Keywords :
Healthcare, Prescription System, Treatment, Diagnosis, Machine Learning, Symptoms, Naive Bayes.
