• Vikrant Waghmare
• Prof. Harish Barapatre
• Mahesh Pimpalkar

Cancer and Diabetes are prolonged diseases which have enormous capability to cause a worldwide health care catastrophe. Various conventional methods, based on physical and chemical tests, are available for diagnosing this disease. Machine learning is an evolving scientific field in data science dealing with the ways in which machines learn from experience. An effective way to classify data is through classification or data mining. This becomes very handy, especially in the medical field where diagnosis and analysis are done through these techniques. The various classification models such as Decision Tree, Artificial Neural Networks, Logistic Regression, Association rules and Naive Bayes are used in this system. The proposed system allows the user to make use of these algorithms to predict the risk of diabetes and Cancer in human body. Based on the results of performed experiments, the Random Forest algorithm shows the highest accuracy with the least error rate. The dataset used is the Pima Indians Data Set, which has the information of patients. The aim this project is to develop a system (a mobile application) which can perform early prediction of Diabetes and Cancer for a patient with a higher accuracy by combining the results of different machine learning techniques

Classifiers, ELM, Disease, Healthcare, Prediction

"Enhancing Performance of Diabetes and Cancer Prediction for a Patient with Higher Accuracy by Combining the Results of Different Classifiers of Machine Learning Techniques ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 9, page no.184-187, September-2020, Available :http://www.jetir.org/papers/JETIR2009023.pdf

"Enhancing Performance of Diabetes and Cancer Prediction for a Patient with Higher Accuracy by Combining the Results of Different Classifiers of Machine Learning Techniques ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 9, page no. pp184-187, September-2020, Available at : http://www.jetir.org/papers/JETIR2009023.pdf