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.

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.

An integrated review on machine learning approaches for heart disease prediction: Direction towards future research gaps

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Fathima Aliyar Vellameeran ◽  
Thomas Brindha
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Future Research ◽  
Disease Prediction ◽  
Learning Approaches ◽  
Research Papers ◽  
Using Data ◽  
Intelligent Methods

Abstract Objectives To make a clear literature review on state-of-the-art heart disease prediction models. Methods It reviews 61 research papers and states the significant analysis. Initially, the analysis addresses the contributions of each literature works and observes the simulation environment. Here, different types of machine learning algorithms deployed in each contribution. In addition, the utilized dataset for existing heart disease prediction models was observed. Results The performance measures computed in entire papers like prediction accuracy, prediction error, specificity, sensitivity, f-measure, etc., are learned. Further, the best performance is also checked to confirm the effectiveness of entire contributions. Conclusions The comprehensive research challenges and the gap are portrayed based on the development of intelligent methods concerning the unresolved challenges in heart disease prediction using data mining techniques.

scholarly journals Developing a Hyperparameter Tuning Based Machine Learning Approach of Heart Disease Prediction

2020 ◽  
Vol 7 (2) ◽  
pp. 631-647
Author(s):  
Emrana Kabir Hashi ◽  
Md. Shahid Uz Zaman
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Models ◽  
Traditional Approach ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
K Nearest Neighbor ◽  
Traditional System ◽  
Prediction Approach

Machine learning techniques are widely used in healthcare sectors to predict fatal diseases. The objective of this research was to develop and compare the performance of the traditional system with the proposed system that predicts the heart disease implementing the Logistic regression, K-nearest neighbor, Support vector machine, Decision tree, and Random Forest classification models. The proposed system helped to tune the hyperparameters using the grid search approach to the five mentioned classification algorithms. The performance of the heart disease prediction system is the major research issue. With the hyperparameter tuning model, it can be used to enhance the performance of the prediction models. The achievement of the traditional and proposed system was evaluated and compared in terms of accuracy, precision, recall, and F1 score. As the traditional system achieved accuracies between 81.97% and 90.16%., the proposed hyperparameter tuning model achieved accuracies in the range increased between 85.25% and 91.80%. These evaluations demonstrated that the proposed prediction approach is capable of achieving more accurate results compared with the traditional approach in predicting heart disease with the acquisition of feasible performance.

Performance Evaluation of Machine Learning Algorithms for Dengue Disease Prediction

2019 ◽  
Vol 16 (12) ◽  
pp. 5105-5110
Author(s):  
S. Kannimuthu ◽  
K. S. Bhuvaneshwari ◽  
D. Bhanu ◽  
A. Vaishnavi ◽  
S. Ahalya
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Severe Illness ◽  
Disease Prediction ◽  
Dengue Disease ◽  
Aedes Mosquitoes ◽  
Disease Prognosis ◽  
C4.5 Algorithm

Dengue is a dangerous disease caused by female mosquitoes. Dengue fever (also called as breakbone fever) is a infection that can cause to a severe illness which is happened by four different viruses and spread by Aedes mosquitoes. It is the necessary to devise effective methodology for dengue disease prognosis. Machine learning is a sub-filed of artificial intelligence (AI) which offers systems the ability to learn and improve from experience without human intervention and being explicitly programmed. In this research work, the performance analysis of various prediction models is done for dengue disease prediction. It is observed that C4.5 algorithm outperforms well in terms of performance measures such as accuracy (89.33%), prediction (88.9%), recall (89.77%) and other measures.

scholarly journals Ensemble model for Heart Disease Prediction

2021 ◽  
Vol 1 (4) ◽  
pp. 268-280
Author(s):  
Bamanga Mahmud , , , Ahmad ◽  
Ahmadu Asabe Sandra ◽  
Musa Yusuf Malgwi ◽  
Dahiru I. Sajoh
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Model ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Ensemble Model ◽  
Prediction Probability

For the identification and prediction of different diseases, machine learning techniques are commonly used in clinical decision support systems. Since heart disease is the leading cause of death for both men and women around the world. Heart is one of the essential parts of human body, therefore, it is one of the most critical concerns in the medical domain, and several researchers have developed intelligent medical devices to support the systems and further to enhance the ability to diagnose and predict heart diseases. However, there are few studies that look at the capabilities of ensemble methods in developing a heart disease detection and prediction model. In this study, the researchers assessed that how to use ensemble model, which proposes a more stable performance than the use of base learning algorithm and these leads to better results than other heart disease prediction models. The University of California, Irvine (UCI) Machine Learning Repository archive was used to extract patient heart disease data records. To achieve the aim of this study, the researcher developed the meta-algorithm. The ensemble model is a superior solution in terms of high predictive accuracy and diagnostics output reliability, as per the results of the experiments. An ensemble heart disease prediction model is also presented in this work as a valuable, cost-effective, and timely predictive option with a user-friendly graphical user interface that is scalable and expandable. From the finding, the researcher suggests that Bagging is the best ensemble classifier to be adopted as the extended algorithm that has the high prediction probability score in the implementation of heart disease prediction.

A Comprehensive survey on Heart Disease Prediction using Machine Intelligence

2021 ◽  
Author(s):  
Santhosh Gupta Dogiparthi ◽  
Jayanthi K ◽  
Ajith Ananthakrishna Pillai
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Models ◽  
Machine Intelligence ◽  
World Health ◽  
World Population ◽  
Disease Prediction ◽  
Validation Parameters ◽  
The World ◽  
Comprehensive Survey

Abstract Objectives: The latest statistics of World Health Organization anticipated that cardiovascular diseases including Coronary Heart Disease, Heart attack, vascular disease as the biggest pandemic to the world due to which one-third of the world population would die. With the emerging AI trends, applying an optimal machine learning model to target early detection and accurate prediction of heart disease is indispensable to bring down the mortality rates and to treat the cardiac patients with best clinical decision support. This stems for the motivation of this paper. This paper presents a comprehensive survey on heart disease prediction models derived and validated out of popular heart disease datasets like Cleveland dataset, Z-Alizadeh Sani dataset. Methods: This survey was performed using the articles extricated from the Google Scholar, Scopus, Web of Science, Research Gate and PubMed search engines between 2005 to 2020. The main keywords for search were Heart Disease, Prediction, Coronary disease, Healthcare, Heart datasets and Machine Learning.Results: This review explores the shortcomings of various approaches used for the prediction of heart diseases. It outlines pros and cons of different research methodologies along with the validation parameters of each reviewed publication.Conclusion: The machine intelligence can serve as a genuine alternative diagnostic method for prediction, which will in turn keep the patients well aware of their illness state. Despite the researcher’s efforts, still uncertainty exist towards standardization of prediction models which demands further exploration of optimal prediction models.

scholarly journals Rice Crop Disease Prediction Using Machine Learning Technique

2021 ◽  
Vol 12 (4) ◽  
pp. 1-15
Author(s):  
Bharati Patel ◽  
Aakanksha Sharaff
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Adaptive Learning ◽  
Large Scale ◽  
Prediction Models ◽  
Crop Yields ◽  
Learning Algorithm ◽  
Plant Phenotyping ◽  
Disease Prediction ◽  
Crop Disease

Crop yields are affected at large scale due to spread of unchecked diseases. The spread of these diseases is similar to the spreading of cancer in human body. But, unlike cancer these diseases can be identified at early stages through plant phenotyping traits analysis. In order to effectively identify these diseases, effective segmentation, feature extraction, feature selection and classification processes must be followed. Selection of the best combination for the given methods is very complex due to the presence of a large number of the aforementioned methods. Thereby disease prediction models are generally found to be ineffective. This paper proposes a highly effective machine learning-based formulation approach to select a proper classification process which improves the overall accuracy of crop disease detection with different dimensionality of plant dataset and included maximum features also. Hence, the proposed adaptive learning algorithm gives 99.2% accuracy compared to other techniques like Back-propagation Neural Network (BPNN), Convolutional Neural Network (CNN), and SVM.

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