Heart disease is the one of the most common disease. This disease is quite common now a days we used different attributes which can relate to this heart diseases well to find the better method to predict and we also used algorithms for prediction. Naive Bayes, algorithm is analyzed on dataset based on risk factors. We also used decision trees and combination of algorithms for the prediction of heart disease based on the above attributes. The results shown that when the dataset is small naive Bayes algorithm gives the accurate results and when the dataset is large decision trees gives the accurate results.
Aim: We aim to treat the poor and downtrodden tribal people of Adilabad district of Telangana State, South India, who are suffering from kidney stones, with locally available plants and with any side effects also without financial burden on them.
Design of the study: Identifying the infected people with kidney stones based on the lab reports, explaining about our treatment, educating them, counseling them, treatment and precautionary measures.
Place and Duration of Study: Adilabad district, Telangana State, South India. Duration of study is for two years that is 2018 to 2019.
Methodology: We have identified around 24 patients (male and female) suffering from kidney stone ailment, but we have presented only six patients details in table form. Treatment is with locally available plants for two days, the results were very much success.
Results: After our treatment the kidney stones, though they are large in size they dissolved and were passed through the urinary tract.
Conclusions: Our treatment is pure with plants without any side effects and free of cost. The patients were very much satisfied with our treatment.
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