Solving diabetes diagnosis problems using machine learning

This research is devoted to the study of the use of machine learning methods to solve the problem of diagnosing diabetes. The results of using machine learning in the context of diabetes are varied and depend on the methods of data analysis, the models used and the quality of the data provided. Experiments on the Diabetes dataset were conducted in the study using a Naive Bayes classifier model and a linear kernel SVM for a binary classification problem. Models are trained on the training dataset, standardizing features, and evaluated on the test set using confusion, precision, recall, F1-measure, and AUC-ROC metrics. The results obtained confirm that machine learning can improve the accuracy of diagnosing diabetes and classifying its type. This allows for customized treatment plans to be developed, considering the unique characteristics of each patient. Machine learning models are also successful in predicting the likelihood of complications, allowing for preventative measures to be taken. Their use facilitates the integration of data from various sources, enriching patient information. In conclusion, machine learning-based decision support systems assist physicians and patients in making informed decisions

Soil fertility evaluation based on the sugeno fuzzy logical model

With the improvement of soils, the productivity of agricultural crops and the efficiency of mineral fertilizers increase; though for individual types of fertilizers the changes take different ways. In different types of soil, different interactions between soil and fertilizersare observed;variouscrop varieties react differently to them, because each variety was bred under one of these interaction conditions, and its influence is phenotypically fixed in it. It was established that the fertility of different types of soils is quantitatively best characterized bystored soil moisture, bulk density, and it is closely related to such generally recognized fertility components as the amount of humus, nitrogen, phosphorus, etc. The main aim of the article is to build a Sugeno fuzzy logical model for assessing soil fertility

Application of the neuro-fuzzy approach to solving problems of soil phases evaluation

The role of variety in the efficient use of fertilizers is enormous. However, most researchers limited their studies to stating the facts about the different productivity of varieties under certain conditions of mineral nutrition. Varieties bear the "imprint" of the conditions in which they are bred. Hence, it is necessary to studythe features of their nutrition, the crop formation, and the quality of products at different forms, doses, ratios, terms and methods of applying mineral fertilizers on various soils of thecultivation zone of a given crop. The main aim of the article is to evaluate soil phases based on the use of a neuro-fuzzy approach. Three soil types were considered: irrigated typical serozem, serozem-meadow soil, and newly irrigated light serozem. A computational experiment was conducted to assess the type of soil, taking into account characteristics such as soil density and humus in percentage terms

Soil fertility evaluation based on the sugeno fuzzy logical model

With the improvement of soils, the productivity of agricultural crops and the efficiency of mineral fertilizers increase;though for individual types of fertilizers the changes take different ways. In different types of soil, different interactions between soil and fertilizersare observed;variouscrop varieties react differently to them, because each variety was bred under one of these interaction conditions, and its influence is phenotypically fixed in it. It was established that the fertility of different types of soils is quantitatively best characterized bystored soil moisture, bulk density, and it is closely related to such generally recognized fertility components as the amount of humus, nitrogen, phosphorus, etc. The main aim of the article is to build a Sugeno fuzzy logical model for assessing soil fertility

Soil fertility evaluation based on the sugeno fuzzy logical model

With the improvement of soils, the productivity of agricultural crops and the efficiency of mineral fertilizers increase;though for individual types of fertilizers the changes take different ways. In different types of soil, different interactions between soil and fertilizersare observed;variouscrop varieties react differently to them, because each variety was bred under one of these interaction conditions, and its influence is phenotypically fixed in it. It was established that the fertility of different types of soils is quantitatively best characterized bystored soil moisture, bulk density, and it is closely related to such generally recognized fertility components as the amount of humus, nitrogen, phosphorus, etc. The main aim of the article is to build a Sugeno fuzzy logical model for assessing soil fertility

Soil fertility evaluation based on the sugeno fuzzy logical model

With the improvement of soils, the productivity of agricultural crops and the efficiency of mineral fertilizers increase; though for individual types of fertilizers the changes take different ways. In different types of soil, different interactions between soil and fertilizersare observed;variouscrop varieties react differently to them, because each variety was bred under one of these interaction conditions, and its influence is phenotypically fixed in it. It was established that the fertility of different types of soils is quantitatively best characterized bystored soil moisture, bulk density, and it is closely related to such generally recognized fertility components as the amount of humus, nitrogen, phosphorus, etc. The main aim of the article is to build a Sugeno fuzzy logical model for assessing soil fertility

Application of the neuro-fuzzy approach to solving problems of soil phases evaluation

The role of variety in the efficient use of fertilizers is enormous. However, most researchers limited their studies to stating the facts about the different productivity of varieties under certain conditions of mineral nutrition. Varieties bear the "imprint" of the conditions in which they are bred. Hence, it is necessary to studythe features of their nutrition, the crop formation, and the quality of products at different forms, doses, ratios, terms and methods of applying mineral fertilizers on various soils of thecultivation zone of a given crop. The main aim of the article is to evaluate soil phases based on the use of a neuro-fuzzy approach. Three soil types were considered: irrigated typical serozem, serozem-meadow soil, and newly irrigated light serozem. A computational experiment was conducted to assess the type of soil, taking into account characteristics such as soil density and humus in percentage terms