scholarly journals Machine Learning Techniques for Thyroid Disease Diagnosis - A Review

2016 ◽  
Vol 9 (28) ◽  
Author(s):  
Shaik Razia ◽  
M. R. Narasinga Rao
Keyword(s):  
Machine Learning ◽  
Thyroid Disease ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Thyroid Disease Diagnosis

scholarly journals A Comparative study of machine learning algorithms on thyroid disease prediction

2018 ◽  
Vol 7 (2.8) ◽  
pp. 315 ◽  
Author(s):  
Shaik Razia ◽  
P SwathiPrathyusha ◽  
N Vamsi Krishna ◽  
N Sathya Sumana
Keyword(s):  
Machine Learning ◽  
Thyroid Hormones ◽  
Thyroid Disease ◽  
High Capacity ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Learning Techniques

Thyroid illness is a medicinal state that influences the functionality of the thyroid organ that is thyroid gland [1](Guyton, 2011).The indications of thyroid ailment differ basing upon the type. There are four most common varieties: hypothyroidism (low capacity) which is caused due to the insufficiency of the thyroid hormones; hyperthyroidism (high capacity) which is caused due to the existence of the thyroid hormones more than just sufficient, basic variations from the norm, most normally an augmentation of the thyroid organ; and tumors which can be benign or can cause cancer. It is additionally conceivable to have irregular thyroid capacity tests with no clinical side effects [2](Bauer & al, 2013).In this study a comparative thyroid disease diagnosis were performed by using Machine learning techniques that is Support Vector Machine (SVM), Multiple Linear Regression, Naïve Bayes, Decision Trees. For this purpose, thyroid disease dataset gathered from the UCI machine learning database was used.

Implementation of machine learning techniques for disease diagnosis

2021 ◽  
Author(s):  
Shachi Mall ◽  
Ashutosh Srivastava ◽  
Bireshwar Dass Mazumdar ◽  
Manmohan Mishra ◽  
Sunil L. Bangare ◽  
...  
Keyword(s):  
Machine Learning ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Review on Machine Learning Techniques for Diagnosis of Heart Disease

2021 ◽  
Vol 9 (8) ◽  
pp. 1418-1420
Author(s):  
Harshal P. Sabale
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Human Health ◽  
World Health Organisation ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
World Health ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Heart Disease Diagnosis

Abstract: Now-a-days, heart disease is becoming a concern to human health. According to World Health organisation (WHO), heart disease is the number one killer among other fatal diseases. Excessive smoking, alcohol consumption and junk food are culprit for the heart disease. Physical inactivity is also a concerning to the human health. Heart disease is pretty hard to predict or diagnose using traditional methods like counselling. But, now-a-days, medical fields are using machine learning to predict or diagnose different diseases. Implementation of machine learning techniques provides faster and mostly accurate results. This can save many life. In this paper, different machine learning approach for heart disease diagnosis are reviewed. Keywords: Heart disease, CVD, Machine Learning

Improving Alzheimer's Disease Diagnosis with Machine Learning Techniques

2011 ◽  
Vol 42 (3) ◽  
pp. 160-165 ◽  
Author(s):  
Lucas R. Trambaiolli ◽  
Ana C. Lorena ◽  
Francisco J. Fraga ◽  
Paulo A.M. Kanda ◽  
Renato Anghinah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Alzheimer’S Disease Diagnosis

scholarly journals Recent Development of Artificial Intelligence Applied in Echocardiography: A Review (Preprint)

2019 ◽  
Author(s):  
Lu Liu ◽  
Ahmed Elazab ◽  
Baiying Lei ◽  
Tianfu Wang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Cardiovascular Diseases ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Tissue Segmentation ◽  
The Past ◽  
Learning Techniques ◽  
The Future

BACKGROUND Echocardiography has a pivotal role in the diagnosis and management of cardiovascular diseases since it is real-time, cost-effective, and non-invasive. The development of artificial intelligence (AI) techniques have led to more intelligent and automatic computer-aided diagnosis (CAD) systems in echocardiography over the past few years. Automatic CAD mainly includes classification, detection of anatomical structures, tissue segmentation, and disease diagnosis, which are mainly completed by machine learning techniques and the recent developed deep learning techniques. OBJECTIVE This review aims to provide a guide for researchers and clinicians on relevant aspects of AI, machine learning, and deep learning. In addition, we review the recent applications of these methods in echocardiography and identify how echocardiography could incorporate AI in the future. METHODS This paper first summarizes the overview of machine learning and deep learning. Second, it reviews current use of AI in echocardiography by searching literature in the main databases for the past 10 years and finally discusses potential limitations and challenges in the future. RESULTS AI has showed promising improvements in analysis and interpretation of echocardiography to a new stage in the fields of standard views detection, automated analysis of chamber size and function, and assessment of cardiovascular diseases. CONCLUSIONS Compared with machine learning, deep learning methods have achieved state-of-the-art performance across different applications in echocardiography. Although there are challenges such as the required large dataset, AI can provide satisfactory results by devising various strategies. We believe AI has the potential to improve accuracy of diagnosis, reduce time consumption, and decrease the load of cardiologists.

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