The thriving medical applications of data mining in the fields of medicine and public health has led to the popularity of its use in knowledge discovery in databases (KDD). Data mining has revealed novel biomedical and healthcare acquaintances for clinical decision making that has great potential to improve the treatment quality of hospitals and increase the survival rate of patients. Disease diagnosis is one of the applications where data mining tools are establishing the successful results. Data mining intends to endow with a systematic survey of current techniques of knowledge discovery in databases using data mining techniques that are in use in today's medical research. Discussion is made to enable the disease diagnosis and the breakthrough of hidden healthcare patterns from related databases is offered. Also, the use of data mining to discover such relationships as those between health conditions and a disease is presented. It further discusses about the tools that can be used for the processing and classification of data. This paper summarizes various technical articles on medical diagnosis and prognosis. It has also been focused on current research being carried out using the data mining techniques to enhance the disease(s) forecasting process. This research paper provides future trends of current techniques of KDD, using data mining tools for healthcare. It also confers significant issues and challenges associated with data mining and healthcare in general. The research found a growing number of data mining applications, including analysis of health care centers for better health policy-making, detection of disease outbreaks and preventable hospital deaths. The root causes of all diseases get closer towards drugs i.e. the foremost risk factor of all hilarious diseases. Drug addiction using WEKA has been used that brings into light concerning majority of drug abusers started abusing drugs at age below 20yrs.It is to make aware the druggist about the various diseases that are caused with heavy or long term intake of drugs in their life. So, to make an expert system that will awake the youth about precarious use of drugs and also alert the affected person.
Brain tumor image segmentation is a play a vital role in the medical field or medical processing. Patient treatment with brain tumors is the significant level determine on early-stage detection of these tumors. Early stage detection of Brain Tumors will enhance the patient lives. The disease of brain tumors by a neurologist frequently uses a manual image segmentation that is a hard and time-consuming process, because of necessary automatic image segmentation. Nowadays, automatic image segmentation is very popular and can solve the issue of tumor brain image segmentation with better performance. The main motive of this research work is to provide a survey of MRI image based brain tumor segmentation techniques. There are various existing study papers, focusing on new techniques for Reasonable Magnetic Image-based brain tumor image segmentation. The main problem is considered a complicated process, because of the variability of tumor area of the complexity of determining the tumor position, size, shape and texture. In this research work, mainly worked on interference method, feature extraction, morphological operators, edge detection methods of gray level and Swarm Ant Lion Optimization based on brain tumor shape growing segmentation to optimize the image complexity and enhance the performance. In new algorithm implemented an inspiring nature method for segmentation of brain tumor image using hybridization of PSOA and ALO is also called a Swarm Ant Lion method. Evaluate the performance metrics with image quality factor (PSNR), Error Rate (MSE), and Exact value (Accuracy Rate). In research work, improve the performance metrics with PSNR and Accuracy Rate and reduce the error rates and compared with the existing method (PNN)
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