Data Mining - Concepts and Techniques.

Prediction of Diabetes using Classification Algorithms

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.

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Survey of Machine Learning Algorithms for Disease Diagnostic

Machine Learning is modern and highly sophisticated technological applications became a huge trend in the industry. Machine Learning is Omni present and is widely used in various applications. It is playing a vital role in many fields like finance, Medical science and in security. Machine learning is used to discover patterns from medical data sources and provide excellent capabilities to predict diseases. In this paper, we review various machine learning algorithms used for developing efficient decision support for healthcare applications. This paper helps in reducing the research gap for building efficient decision support system for medical applications.

Machine Learning in Healthcare: A Review

Diabetes Prediction using Machine Learning Algorithms

Diabetes has affected over 246 million people worldwide with a majority of them being women. According to the WHO report, by 2025 this number is expected to rise to over 380 million. The disease has been named the fifth deadliest disease in the United States with no imminent cure in sight. With the rise of information technology and its continued advent into the medical and healthcare sector, the cases of diabetes as well as their symptoms are well documented. This paper aims at finding solutions to diagnose the disease by analyzing the patterns found in the data through classification analysis by employing Decision Tree and Naive Bayes algorithms. The research hopes to propose a quicker and more efficient technique of diagnosing the disease, leading to timely treatment of the patients.

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Diagnosis of diabetes using classification mining techniques

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Diagnosis of Diabetes Using Classification Mining Techniques

is a big issue all around the world. It is a disease, which is fatal in many cases and has affected the lives of many and will continue to affect the lives of many more. Breast cancer represents the second primary cause of cancer deaths in women today and has become the most common cancer among women both in the developed and the developing world in the last years. 40,000 women die in a year from this disease, which is one woman every 13 minute dying from this disease everyday. Early detection of breast cancer is far easier to cure. This paper presents a decision tree based data mining technique for early detection of breast cancer. Breast cancer diagnosis differentiates benign (lacks ability to invade neighboring tissue) from malignant (ability to invade neighboring tissue) breast tumors. This paper also discusses various data mining approaches that have been utilized for breast cancer diagnosis, and also summarizes breast cancer in general (types, risk factors, symptoms and treatment).

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Ronak Sumbaly

Papers

Diagnosis of diabetes using classification mining techniques

Diagnosis of Diabetes Using Classification Mining Techniques

Diagnosis of Breast Cancer using Decision Tree Data Mining Technique