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Pal, Saurabh
- Discovery of Hidden Pattern in Thyroid Disease by Machine Learning Algorithms
Authors
1 VBSPU Jaunpur, U.P., IN
Source
Indian Journal of Public Health Research & Development, Vol 11, No 1 (2020), Pagination: 61-66Abstract
Background: Decision tree provides help in making decision for very complex and large dataset. Decision tree techniques are used for gathering knowledge. Classification tree algorithms predict the experimental values of women thyroid dataset. The objective of this research paper observation is to determine hyperthyroidism, hypothyroidism and euthyroidism participation in hormones can be good predictor of the final result of laboratories and to examination whether the propose ensemble approach can be similar accuracy to other single classification algorithm.
Results: In the proposed experiment real data from 499 thyroid patients were used classifications algorithms in predicting whether thyroid detected or not detected on the basis of T3, T4 and TSH experimental values. The results show that the expectation of maximization classification tree algorithms in those of the best classification algorithm especially when using only a group of selected attributes. Finally we predict batch size, tree confidential factor, min number of observation, num folds, seed, accuracy and time build model with different classes of thyroid sickness.
Conclusion: Different classification algorithms are analyzed using thyroid dataset. The results obtained by individual classification algorithms like J48, Random Tree and Hoeffding gives accuracy 99.12%, 97.59% and 92.37 respectively. Then we developed a new ensemble method and apply again on the same dataset, which gives a better accuracy of 99.2% and sensitivity of 99.36%. This new proposed ensemble method can be used for better classification of thyroid patients.
Keywords
J48, Random Tree, Hoeffding, Prediction, T3, T4, TSH, Hypothyroidism, Hyperthyroidism, Euthyroidism and Ensemble Model.
- Skin Diseases Prediction:Binary Classification Machine Learning & Multi Model Ensemble Techniques
Authors
1 Research Scholor, MCA Dept., VBS Purvanchal University, Jaunpur, IN
2 Dept. of MCA, VBS Purvanchal University, Jaunpur, UP, IN
Source
Indian Journal of Public Health Research & Development, Vol 11, No 1 (2020), Pagination: 737-742Abstract
Unlike many other diseases, the skin disease has more irritability. Dermatology sicknesses incorporates normal skin rashes to serious skin contaminations, which happens because of scope of things, like diseases, warm, allergens, framework issue and drugs. First regular skin issue are dermatitis. Atopic dermatitis is relating current (perpetual) condition that causes eager, aroused skin. Most much of the time it appears as patches on the face, neck, trunk or appendages. It will in general erupt sporadically so die down for a period. A large portion of the dermatological sicknesses are not reparable but rather most the treatments depend on the administration of the side effects related with it.
The focus of this research will be the Dermatology database. The problem is to determine the type of Eryhemato-Squamous disease like psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, cronic dermatitis and pityriasis rubra pilaris. The differential analysis of erythemato-squamous maladies is a genuine issue in dermatology. They all offer the clinical highlights of erythema and scaling, with next to no distinctions. Each pattern is a set of 33 numbers in the range linear values and one of them is nominal. The 80% of the dataset utilize for demonstrating and keep down 20% for approval. Objective is to accomplish best performer algorithm which will convey in dermatology informational collection so for this reason the gut feel recommends distance based calculations like k-Nearest Neighbors and Support Vector Machines may progress admirably. By using 10-fold cross validation and assess calculations utilizing the accuracy metric.