Thyroid Disease Prediction Using Machine Learning Approaches

2020 ◽  
Author(s):  
Gyanendra Chaubey ◽  
Dhananjay Bisen ◽  
Siddharth Arjaria ◽  
Vibhash Yadav
Keyword(s):  
Machine Learning ◽  
Thyroid Disease ◽  
Disease Prediction ◽  
Learning Approaches

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 Assessment of Classifiers with Machine Learning based Thyroid Disease Prediction System

2020 ◽  
Vol 8 (5) ◽  
pp. 4718-4721
Keyword(s):  
Machine Learning ◽  
Thyroid Disease ◽  
System Model ◽  
Disease Prediction ◽  
Prediction System ◽  
The People ◽  
Different Types ◽  
Antithyroid Treatment ◽  
Roc Area ◽  
F Measure

Most of the people in different nations are suffering from Thyroid related diseases and these are lifelong. Many people are unaware of having Thyroid related diseases. Main cause for this is due to improper functioning of Thyroid gland secreting Thyroid hormone which regulates body metabolism. In this paper we have made survey on classifiers like Decision Tree C4.5(J48), Multilayer Perceptron, Naïve Bayes by measuring TP Rate, FP Rate, Precision, Recall, F-Measure, MCC, ROC Area, PRC Area and developed a prediction system for Thyroid diseases. For training and testing the classifiers we have used Thyroid dataset from UCI repository. Dataset consists of 9172 records containing 29 attribute values and 1 diagnosis class value. The diagnosis class value consists of different types Thyroid disease conditions like hyperthyroid conditions, hypothyroid conditions, binding protein, general health, replacement therapy, antithyroid treatment and miscellaneous. The proposed prediction system model capable of predicting type of Thyroid disease whether a person is suffering or not.

Utilization of Machine Learning Algorithms for Thyroid Disease Prediction

2021 ◽  
pp. 57-69
Author(s):  
Md. Shahajalal ◽  
Md. Masudur Rahman ◽  
Sk. Arifuzzaman Pranto ◽  
Romana Rahman Ema ◽  
Tajul Islam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Thyroid Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Disease Prediction

scholarly journals Disease Prediction and Doctor Recommendation System using Machine Learning Approaches

2021 ◽  
Vol 9 (VII) ◽  
pp. 34-44
Author(s):  
Anand Kumar
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Recommendation System ◽  
Disease Patient ◽  
Classification Algorithms ◽  
Disease Prediction ◽  
Learning Approaches ◽  
Interactive Interface ◽  
Front End ◽  
Doctor Recommendation

In our everyday life we go over numerous individuals who are experiencing some sort of Diseases. Prediction of disease is an integral part of treatment. In this project the disease is accurately predicted by looking at the symptoms of the patient where the patient can input his/her symptom and the system will predict the disease patient is suffering from. Classification Algorithms like the Naïve Bayes (NB), Random Forest, Logistic Regression and KNN have been broadly utilized to anticipate the Disease, where different accuracies were obtained. In corresponding to a particular Disease, for example, Heart Disease, Diabetes and so on is additionally anticipated by demonstrating “True” or “False” i.e. if an individual is having or not having that Disease. Prediction of such a system can have a very large potential in the medical treatment of the future. Once the Disease is predicted by the system, It then recommends which type of doctor to consult. In this paper, an audit of some new works identified with utilization of Machine Learning in expectation of disease is predicted. An interactive interface is built as front-end to facilitate interaction with the symptoms. The whole model is implemented using Django and is connected to the Django Server.

Machine Learning Approaches for Cardiovascular Disease Prediction

2021 ◽  
pp. 75-84
Author(s):  
Stuti Pandey ◽  
Abhay Kumar Agarwal
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Human Life ◽  
Medical Science ◽  
Second Primary ◽  
Disease Prediction ◽  
Learning Approaches ◽  
Prediction Systems ◽  
Medical Analysis

In a human body, the heart is the second primary organ after the brain. It causes either a long-term impairment or death of a person if suffering from a cardiovascular disease. In medical science, a proper medical analysis and examination of a cardiovascular disease is very crucial, convincing, and sophisticated task for saving a human life. Data analytics rises because of the absence of sufficient practical tools for exploring the trends and unknown relationships in e-health records. It predicts and achieves information which can ease the diagnosis. This survey examines cardiovascular disease prediction systems developed by different researchers. It also reviews the trend of machine learning approaches used in the past decade with results. Related studies comprise the performance of various classifiers on distinct datasets.

Heart Disease Prediction Using Machine Learning

2021 ◽  
pp. 373-381
Author(s):  
Shiva Shanta Mani B. ◽  
Manikandan V. M.
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Early Stage ◽  
Heart Diseases ◽  
Age Groups ◽  
Disease Prediction ◽  
Learning Approaches ◽  
Health Issues ◽  
Decision Tree Classifier ◽  
Knn Classifier

Heart disease is one of the most common and serious health issues in all the age groups. The food habits, mental stress, smoking, etc. are a few reasons for heart diseases. Diagnosing heart issues at an early stage is very much important to take proper treatment. The treatment of heart disease at the later stage is very expensive and risky. In this chapter, the authors discuss machine learning approaches to predict heart disease from a set of health parameters collected from a person. The heart disease dataset from the UCI machine learning repository is used for the study. This chapter discusses the heart disease prediction capability of four well-known machine learning approaches: naive Bayes classifier, KNN classifier, decision tree classifier, random forest classifier.

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