scholarly journals Disease Prediction from Drug Information using Machine Learning

2021 ◽  
Vol 1 (4) ◽  
Keyword(s):  
Machine Learning ◽  
Drug Information ◽  
Disease Prediction

scholarly journals Disease Prediction from Drug Information using Machine Learning

2021 ◽  
Author(s):  
KOUSHIK DEB
Keyword(s):  
Machine Learning ◽  
Healthcare Professional ◽  
Healthcare Professionals ◽  
Prediction Models ◽  
Drug Information ◽  
Disease Type ◽  
Online Review ◽  
Disease Prediction ◽  
Care Information ◽  
Concise Information

Drug reviews play a very important role in providing crucial medical care information for both healthcare professionals and consumers. Also, in the absence of an actual practicing healthcare professional, a consumer can look for an online review of drugs before making a purchase. But these reviews are generally unstructured in nature and often do not provide concise information on the disease/nature of the disease, the drugs are prescribed for. In this scenario, a learning model that can be trained to predict the disease/type of disease, when provided witha drug name and its corresponding review, becomes very important. To mitigate the above-mentioned issue, we present and compare various machine learning-based prediction models. Also, the performance of each of the models has been quantified using metrics such as precision, recall, F1-Score, and accuracy.

scholarly journals Disease Prediction from Drug Information using Machine Learning

2021 ◽  
Vol 1 (4) ◽  
pp. 16-21
Author(s):  
Shuvendu Das ◽  
Sainik Kumar Mahata ◽  
Abhishek Das ◽  
Koushik Deb
Keyword(s):  
Machine Learning ◽  
Healthcare Professional ◽  
Healthcare Professionals ◽  
Prediction Models ◽  
Drug Information ◽  
Disease Type ◽  
Online Review ◽  
Disease Prediction ◽  
Care Information ◽  
Concise Information

Drug reviews play a very important role in providing crucial medical care information for both healthcare professionals and consumers. Also, in the absence of an actual practicing healthcare professional, a consumer can look for an online review of drugs before making a purchase. But these reviews are generally unstructured in nature and often do not provide concise information on the disease/nature of the disease, the drugs are prescribed for. In this scenario, a learning model that can be trained to predict the disease/type of disease, when provided with a drug name and its corresponding review, becomes very important. To mitigate the above-mentioned issue, we present and compare various machine learning-based prediction models. Also, the performance of each of the models has been quantified using metrics such as precision, recall, F1-Score, and accuracy.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

scholarly journals Machine Learning Based Classification Models for Heart Disease Prediction

2021 ◽  
Vol 1916 (1) ◽  
pp. 012092
Author(s):  
N Karthikeyan ◽  
P Padmanaban ◽  
A Prasanth ◽  
D Ragunath
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Disease Prediction ◽  
Classification Models
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