Breast cancer and heart disease can be acknowledged as very dangerous and common disease in many countries including Pakistan. In this paper classifiers comparative study has been performed for the tumor and heart disease classification. Around one lac women are diagnosed annually with this life-threatening disease having no family history of the disease. If it is not treated on time it may grow and spread to the other parts of human body. Mammograms are the X-rays of the breast which can be used for the screening of cancer tumor. Prior identification of breast cancer may increase the chance of survival up to 70 percent. Tumors which causes cancer can be categorized into two types: a) Benign and b) Malignant. Benign tumor can be explained as the tumor which are not attached to neighbor tissues or spread in the other parts of the body. In Malignant tumor, other parts may be affected by it as it can grow and spread in the other parts of the body. To classify the tumor as Malignant or Benign is very complex as the similarities of cancer tumor and tumor caused by the skin inflammation are almost same. The early identification of Malignant is mandatory to protect the patient life. Diversified medical methods based on deep learning and machine learning have been developed to treat the patients as cancer is a very serious and crucial issue in this era. In this research paper machine learning algorithms like logistic regression, K-NN and tree have been applied to the breast cancer data set which has been taken from UCI Machine learning repository. Comparative study of classifiers has been performed to determine the better classifier for the robust prediction of breast tumors. Simulated results proved that using Logistic regression, ninety-one percent accuracy was achieved. The research showed that logistic regression can be applied for the accurate and precise early prediction of breast cancer. Cardiovascular disease is very common throughout the world. It has been noticed that health in cardiac patients that there are so many factors which causes heart disease or heart attack. The factors leading to the heart failure includes varying blood pressure, high sugar, cardiac pain, and heart rate, high cholesterol level (LDL), artery blockage and irregular ECG signals. Many researchers proved that stress in patients can also be the reason for the heart disease. Higher numbers of cardiac surgeries like angioplasty and heart bypass are performed on annual basis. Actually, people don't care about their lifestyle and diet and fully ignore the symbols. It can be early predicted and cured if proper testing and medication for heart is done. Sometimes there is a false pain which has the same feeling like angina pain depicting cardiovascular disease. To reduce the false alarm and robustly classify the heart disease, several machine learning approaches have been adopted. In proposed research for the accurate classification of heart disease comparison has been performed among support vector machine (SVM), K-nearest neighbors K-NN and lin...
Arm rehabilitation activities necessary to be continuously monitored in order to provide information of rehabilitation results to be examined by physical therapist. The determination of monitoring is to enhancing rehabilitation process.Moreover, a portable and simple home-based rehabilitation device can help patients to improve daily rehabilitation process activity. Some earlier studies regarding home-based rehabilitation process have shown improvement in promoting human movement recovery. This paper focuses on the development of a measurement by using arms guard and Smart Glove or Rehabilitation Glove, to assist stroke patient because of some complications such as accident and disease. This paper concourses on the method and application of mechanical equipment, sensors equipped Rehabilitation Glove measurement gripping activities. The devices will move based on a human operator's finger and arms movement using the Rehabilitation Glove. The system development involves a Microcontroller and HyperTerminal as a core processing for the instrumentation, communication and controlling applications. A series of bend or known as flex force sensors are fitted in a Rehabilitation Glove to get reading from the movement of human fingers. The quality of the force feedback is strongly affected by the maximum torque measurable by the Rehabilitation Glove and the performance of the force controller. Finally, the intelligence, learning and experience aspects of the human can be combined with the strength, endurance and speed of the human arms and Rehabilitation sensor in order to generate proper output of this paper.
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