Survey of Machine Learning Algorithms for Disease Diagnostic

By Meherwar Fatima, Maruf Pasha. 2017

616 citations recorded by the source corpus.

In medical imaging, Computer Aided Diagnosis (CAD) is a rapidly growing dynamic area of research. In recent years, significant attempts are made for the enhancement of computer aided diagnosis applications because errors in medical diagnostic systems can result in seriously misleading medical treatments. Machine learning is important in Computer Aided Diagnosis. After using an easy equation, objects such as organs may not be indicated accurately. So, pattern recognition fundamentally involves learning from examples. In the field of bio-medical, pattern recognition and machine learning promise the improved accuracy of perception and diagnosis of disease. They also promote the objectivity of decision-making process. For the analysis of high-dimensional and multimodal bio-medical data, machine learning offers a worthy approach for making classy and automatic algorithms. This survey paper provides the comparative analysis of different machine learning algorithms for diagnosis of different diseases such as heart disease, diabetes disease, liver disease, dengue disease and hepatitis disease. It brings attention towards the suite of machine learning algorithms and tools that are used for the analysis of diseases and decision-making process accordingly.

Subfields: Artificial Intelligence, Health Information Management

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Chao Yu
2. Jiming Liu
3. Shamim Nemati
4. Ala Al-Fuqaha
5. Junaid Qadir
6. Ersin Yumer
7. Muhammad Bilal
8. T. Marwick

In Health Information Management

1. Muhammad Bilal
2. Ala Al-Fuqaha
3. Junaid Qadir
4. Meghavi Rana
5. Megha Bhushan
6. Adnan Qayyum

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, background): “ently call for a significant revolution of the diagnostic process, leading to the advent of automated diagnostic era that is fueled by advanced big data analysis and machine learning techniques [241], [242], [243]. Normally formulated as a supervised classification problem, existing machining learning”
• Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints (2019, background): “Conservative prognostications of dataset sizes – particularly for practical endeavours such as self-driving cars [2], assistive medical robots [61], and medical analysis [18] – suggest one will train on datasets orders of magnitude larger than those that are publicly available today.”
• Machine learning and deep learning approach for medical image analysis: diagnosis to detection (2022, background): “Multiple diseases like diabetes, heart disease, liver disease, dengue and hepatitis were identified by recognizing the pattern in the available data and classifying them further using ML classifiers [29, 27, 47].”
• Secure and Robust Machine Learning for Healthcare: A Survey (2020, background): “different diseases (such as diabetes, dengue, hepatitis, heart, and liver) is presented in [67].”
• Artificial Intelligence in Cardiovascular Imaging (2019, methodology): “Fully automated diagnostic tools will ultimately provide medical opinions (12,26), but will also require extensive validation before regulatory approval.”